accueil / serveurs / Finance & Paiement / com.stratalize/intelligence
com.stratalize/intelligence
serveur MCP (Model Context Protocol) · stratalize-intelligence 1.0.0
Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur.
Est-ce que com.stratalize/intelligence est sûr ?
Au dernier audit MCP Hub, le serveur MCP « com.stratalize/intelligence » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.
Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.
Les 201 outils
- get_13f_holdings_delta≈ 152 tokens
Use when comparing institutional 13F-HR filing quarters for QoQ holdings change context. Returns current and prior quarter filing pairs with delta summary scaffolding. Source: SEC EDGAR 13F submissions. Requires ticker or cik. $0.50 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescik· string · optionnelticker· string · optionnel
Exemple d'appel{"name": "get_13f_holdings_delta", "arguments": {"cik": "texte", "ticker": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_8k_material_events≈ 180 tokens
Use when monitoring material issuer events via recent 8-K filings for a ticker. Returns 8-K filing metadata from EDGAR submissions with optional item-code filter hints. Source: SEC EDGAR submissions. $0.10 USDC per call.
Paramètres, retour & exemple (3)
Paramètresticker· string · requisdays_back· integer · optionnelitem_codes· array · optionnel
Exemple d'appel{"name": "get_8k_material_events", "arguments": {"ticker": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_adoption_stage≈ 205 tokens
Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_adoption_stage", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_agricultural_commodity_benchmark≈ 196 tokens
Spot agricultural commodity price from FRED IMF primary commodity series for soybeans, wheat, corn, cotton, or coffee. Returns USD price, unit, and observation period for crop hedging, food cost modeling, and trade exposure agents. Source: FRED / IMF. $0.02 atomic. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescommodity· string · requis · soybeans | wheat | corn | cotton | coffee
Exemple d'appel{"name": "get_agricultural_commodity_benchmark", "arguments": {"commodity": "soybeans"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_ai_consensus_on_topic≈ 210 tokens
Use when researching how AI systems characterize a vendor, category, trend, or business topic across multiple platforms simultaneously. Returns consensus score, sentiment mix, key themes, and platform-by-platform breakdown. Example: AI in healthcare scores 0.78 consensus — key themes: clinical decision support, administrative automation, prior auth reduction — high consensus signals established narrative safe for board communications. Source: Stratalize AI citation composite. $0.50 USDC per call.
Paramètres, retour & exemple (2)
Paramètrestopic· string · requiscategory· string · optionnel
Exemple d'appel{"name": "get_ai_consensus_on_topic", "arguments": {"topic": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_ai_regulatory_landscape≈ 247 tokens
Use when standing up or auditing an AI governance program and need one signed snapshot across NIST AI RMF, EU AI Act, US state AI legislation, Colorado AI Act, model risk management standards, and UK FCA PS7/24 coverage. All inputs optional — returns full framework composite; optional nist_function, state, system_type, institution_type filters. $3.00 USDC per call.
Paramètres, retour & exemple (4)
Paramètresstate· string · optionnelsystem_type· string · optionnelnist_function· string · optionnel · GOVERN | MAP | MEASURE | MANAGEinstitution_type· string · optionnel · community_bank | regional_bank | large_bank | credit_union | insurance_company | asset_manager | fintech
Exemple d'appel{"name": "get_ai_regulatory_landscape", "arguments": {"state": "texte", "system_type": "texte", "nist_function": "GOVERN", "institution_type": "community_bank"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_aml_regulatory_benchmark≈ 177 tokens
AML regulatory benchmarks — FinCEN SAR filing rates, OFAC SDN counts and recent additions, BSA enforcement fine history, travel rule thresholds, and compliance staffing benchmarks. For compliance agents and financial institution risk officers. $0.50 USDC per call.
Paramètres, retour & exemple (2)
Paramètresfocus· string · optionnel · enforcement | sar_filing | ofac | allinstitution_type· string · optionnel · bank | crypto_exchange | money_services | all
Exemple d'appel{"name": "get_aml_regulatory_benchmark", "arguments": {"focus": "enforcement", "institution_type": "bank"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_asc_benchmark≈ 188 tokens
Use when benchmarking ASC financial performance, evaluating an ASC acquisition, or preparing an administrator board report. Returns cost per case medians and revenue mix percentages by specialty. Example: Orthopedic ASC cost per case median $4,200 — facilities above $5,100 are in the bottom cost quartile — orthopedic mix at 60% of cases maximizes margin vs ophthalmology-heavy mix. Source: ASCA and CMS 2024 composite. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresstate· string · optionnelspecialty· string · optionnel
Exemple d'appel{"name": "get_asc_benchmark", "arguments": {"state": "texte", "specialty": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_audit_fee_benchmark≈ 223 tokens
Use when benchmarking audit costs, evaluating auditor proposals, or preparing an audit committee RFP. Audit fee benchmarks — total fees and fees as a percentage of revenue by company revenue band and auditor tier (Big 4 vs national vs regional). Source: Audit Analytics public aggregate data. Used by CFOs and audit committees in auditor RFPs and fee negotiations. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresindustry· string · optionnelauditor_tier· string · optionnel · big4 | national | regionalannual_revenue_usd· number · requis
Annual revenue in USD, e.g. 50000000 for $50M
Exemple d'appel{"name": "get_audit_fee_benchmark", "arguments": {"annual_revenue_usd": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_bank_counterparty_diligence≈ 294 tokens
Use when diligencing a bank or financial institution counterparty before onboarding, funding, or transaction approval. Fans in bank regulatory benchmark, AML regulatory benchmark, OCC enforcement actions, credit union peer benchmark, and CFPB complaint intelligence — degrading per-section on source_unavailable. Input institution_name (required). $3.00 USDC per call.
Paramètres, retour & exemple (5)
Paramètresaml_focus· string · optionnel · enforcement | sar_filing | ofac | allasset_size_tier· string · optionnel · community_under_1b | regional_1b_10b | large_regional_10b_100b | money_center_over_100binstitution_name· string · requisinstitution_type· string · optionnel · bank | crypto_exchange | money_services | allcredit_union_asset_tier· string · optionnel · under_100m | 100m_500m | 500m_1b | over_1b
Exemple d'appel{"name": "get_bank_counterparty_diligence", "arguments": {"institution_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_bank_financial_intelligence≈ 216 tokens
Use when evaluating a bank for acquisition, partnership, correspondent banking, or competitive analysis in a local market. Returns FDIC-sourced assets, deposits, capital ratios, loan quality, and peer benchmark positioning. Example: Midwest Community Bank — $2.4B assets, CET1 12.3% (well above 6% minimum), NPL ratio 0.42% vs 0.71% peer median — strong capital position, favorable acquisition target profile. Source: FDIC BankFind synced call report data. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètresbank_name· string · requis
e.g. JPMorgan, Wells Fargo, First National Bank
Exemple d'appel{"name": "get_bank_financial_intelligence", "arguments": {"bank_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_bank_regulatory_benchmark≈ 202 tokens
Bank regulatory capital and financial performance benchmarks — CET1, Tier 1 leverage, NIM, efficiency ratio, charge-off rates, and loan-to-deposit ratio by asset size tier. Source: FDIC call report public aggregates. For bank CFOs, risk officers, and bank analysts. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresbank_type· string · optionnel · commercial | savings | cooperativeasset_size_tier· string · requis · community_under_1b | regional_1b_10b | large_regional_10b_100b | money_center_over_100b
Exemple d'appel{"name": "get_bank_regulatory_benchmark", "arguments": {"asset_size_tier": "community_under_1b"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_billing_coding_risk≈ 226 tokens
Use when assessing coding compliance risk before an OIG audit, preparing for a RAC review, or building a revenue integrity program. Returns E/M distribution benchmarks, upcoding risk signals, OIG audit priority themes, and RAC watchlist. Example: Cardiology practice E/M mix at 67% level 4/5 visits vs 48% national benchmark — flagged HIGH upcoding risk — OIG cardiology audit focus active in 2024-2025 cycle. Source: CMS and OIG compliance composite. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresspecialty· string · optionnelannual_claim_volume· number · optionnellevel_4_5_percentage· number · optionnel
Percentage of E/M claims at level 4 or 5
Exemple d'appel{"name": "get_billing_coding_risk", "arguments": {"specialty": "texte", "annual_claim_volume": 1, "level_4_5_percentage": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_bls_inflation_components≈ 289 tokens
Use when analyzing inflation exposure by spending category, structuring or reviewing vendor contract escalation clauses, benchmarking healthcare or real estate cost inflation, or providing monetary policy context for a CFO or treasury brief. Medical care CPI and housing CPI consistently diverge from headline inflation — critical for healthcare budget planning and commercial lease negotiations. Example: Medical care CPI +3.8% YoY vs headline CPI +3.1% — healthcare costs inflating 23% faster than the general economy, directly driving hospital operating budget overruns in fixed-price service contracts. Source: Bureau of Labor Statistics CPI — the Federal Reserve's primary inflation benchmark. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescategory· string · optionnel · all_items | food | energy | medical_care | housing | transportation | education | apparel
Exemple d'appel{"name": "get_bls_inflation_components", "arguments": {"category": "all_items"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_bls_sector_employment≈ 308 tokens
Use when benchmarking workforce planning against sector labor market conditions, assessing industry growth trajectory for strategic planning, providing economic context for board reporting, or evaluating talent acquisition timing for a specific industry. Returns BLS payroll employment by major sector with month-over-month change, year-over-year change, and trend classification from the official establishment survey covering 650,000 US worksites — the same data the Federal Reserve uses to assess labor market conditions. Example: Healthcare sector — 8.41M employed, +47K MoM, +3.2% YoY, EXPANDING for 14 consecutive months — persistent hiring demand supports above-market compensation benchmarks. Source: Bureau of Labor Statistics Current Employment Statistics. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètressector· string · requis · manufacturing | healthcare | financial_services | construction | retail | technology | government | all_private
Exemple d'appel{"name": "get_bls_sector_employment", "arguments": {"sector": "manufacturing"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_brand_momentum≈ 186 tokens
Use when monitoring a vendor brand trajectory in AI recommendations or tracking week-over-week competitor momentum for a CMO brief. Returns 4-week momentum score, trend direction, and weekly movement series. Example: HubSpot 4-week momentum +1.8, GROWING trend — 3 consecutive weeks of citation increase following major product launch — competitive signal requiring CMO attention. Source: Stratalize brand index. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètresbrand_name· string · requis
Exemple d'appel{"name": "get_brand_momentum", "arguments": {"brand_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cac_benchmark≈ 281 tokens
Use when evaluating sales and marketing efficiency, setting CAC targets, or benchmarking GTM performance before a board review. Returns CAC payback ranges, LTV/CAC guardrails, and channel efficiency benchmarks by industry and GTM motion. Example: Mid-market SaaS with field sales — median CAC payback 22 months, LTV/CAC 3.8x — organizations above 30-month payback face capital efficiency pressure from investors. Source: Stratalize go-to-market composite. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresindustry· string · requis · saas | healthcare | legal | financial_services | manufacturing | retail | real_estate | education
Industry verticalgtm_motion· string · optionnel · product_led | sales_led | channelavg_contract_value_usd· number · optionnel
ACV for LTV:CAC calculation
Exemple d'appel{"name": "get_cac_benchmark", "arguments": {"industry": "saas"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cap_rate_benchmark≈ 224 tokens
Commercial real estate cap rate benchmarks by asset class, market tier, and geography. Source: CBRE and JLL quarterly cap rate surveys. Used by CRE acquisition teams, asset managers, and real estate CFOs for property pricing and portfolio valuation. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresregion· string · optionnel · northeast | southeast | midwest | southwest | westasset_class· string · requis · multifamily | industrial | office | retail_strip | retail_mall | hospitality | self_storage | senior_housingmarket_tier· string · optionnel · gateway | secondary | tertiary
Exemple d'appel{"name": "get_cap_rate_benchmark", "arguments": {"asset_class": "multifamily"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_case_law_synthesis≈ 236 tokens
Federal case law synthesis across US courts via CourtListener. Returns top cases by relevance for a legal topic with citation counts, court, and precedential status. Use for legal research agents, litigation intelligence, and compliance gap analysis. Source: CourtListener 60M+ opinions. $0.50. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.50 USDC per call.
Paramètres, retour & exemple (4)
Paramètrescourt· string · optionnellimit· integer · optionneltopic· string · requisdate_after· string · optionnel
Exemple d'appel{"name": "get_case_law_synthesis", "arguments": {"topic": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_category_ai_leaders≈ 189 tokens
Use when assessing brand visibility in AI-generated recommendations or researching which vendors dominate AI platform responses in a software category. Returns vendors ranked by unprompted AI mention frequency. Example: CRM category — Salesforce 42 mentions across 100 queries, HubSpot 28, Microsoft Dynamics 14 — Salesforce dominates AI recommendations by 50% over nearest competitor. Source: Stratalize AI citation index. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescategory· string · requis
Exemple d'appel{"name": "get_category_ai_leaders", "arguments": {"category": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_category_disruption_signal≈ 202 tokens
Use when assessing whether a software category faces near-term displacement risk, or timing a market entry or exit decision. Returns disruption risk score from 0 to 1 with evidence strings from citation volume patterns. Example: ERP category — disruption risk score 0.71, evidence: 34 citations referencing AI-native alternatives, 12 referencing no-code replacements — HIGH disruption risk for legacy on-premise vendors. Source: Stratalize citation volume heuristics. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescategory· string · requis
Exemple d'appel{"name": "get_category_disruption_signal", "arguments": {"category": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_category_spend_benchmark≈ 207 tokens
Use when benchmarking total spend in a software category against same-size peers. Returns median monthly spend, p25/p75 band, and sample size for any software category by company size. Example: Mid-market CRM spend median ~$3,500/mo, p75 of $4,900 — organizations above p75 have a negotiation mandate supported by market data. Source: Stratalize enterprise spend composite. $0.10 USDC per call.
Paramètres, retour & exemple (3)
Paramètrescategory· string · requis
Software or service categoryindustry· string · optionnelcompany_size· string · optionnel
Exemple d'appel{"name": "get_category_spend_benchmark", "arguments": {"category": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cfpb_complaint_intelligence≈ 214 tokens
Use when assessing consumer finance risk, benchmarking complaint volume against peers, or conducting pre-acquisition due diligence on a financial institution. Returns CFPB complaint rollups by company and product — volume, issue themes, and response rate trends. Example: Regional Bank X — 847 CFPB complaints in 2023, 34% on mortgage servicing, complaint volume 2.3x peer median — elevated consumer protection risk signal. Source: CFPB Consumer Complaint Database synced data. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresproduct· string · optionnelcompany_name· string · requis
Exemple d'appel{"name": "get_cfpb_complaint_intelligence", "arguments": {"company_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_chain_tvl_benchmark≈ 154 tokens
Live TVL by blockchain — Ethereum, Base, Solana, Arbitrum, and 50+ chains from DeFiLlama. Rankings, 1D and 7D change, protocol counts, Ethereum dominance, and Base vs ETH TVL comparison for x402 agent context. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètreslimit· number · optionnelsort_by· string · optionnel · tvl | change_1d | change_7d
Exemple d'appel{"name": "get_chain_tvl_benchmark", "arguments": {"limit": 1, "sort_by": "tvl"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_citation_authority≈ 176 tokens
Citation authority analysis — how many times a case has been cited, by which courts, and authority signal (landmark / highly cited / frequently cited / limited). Source: CourtListener. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.10 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescitation· string · optionnelcase_name· string · optionnel
Exemple d'appel{"name": "get_citation_authority", "arguments": {"citation": "texte", "case_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_climate_risk_benchmark≈ 198 tokens
Climate financial risk benchmarks — physical risk (flood, hurricane, wildfire, heat), transition risk (carbon pricing scenarios, stranded assets), and lender implications. Source: FEMA NFIP, NGFS scenarios. For ESG and risk agents. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresregion· string · optionnel · southeast | northeast | midwest | southwest | west | nationalrisk_type· string · optionnel · physical | transition | allproperty_type· string · optionnel · commercial | residential | industrial | all
Exemple d'appel{"name": "get_climate_risk_benchmark", "arguments": {"region": "southeast", "risk_type": "physical", "property_type": "commercial"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_climate_risk_score≈ 267 tokens
Use when pricing physical climate risk for a location — real estate acquisition, commercial property underwriting, construction site selection, or climate-related financial disclosure. Returns a composite risk score across six perils (flood, hurricane, tornado, wildfire, extreme heat, freeze) using the same risk factors embedded in FEMA's National Risk Index. Example: Miami-Dade FL — EXTREME overall, top 2% hurricane exposure, Zone AE flood designation across 40% of commercial parcels, 94 days above 95°F annually — commercial property insurance costs 3.2x national median. Source: NOAA Climate Normals, FEMA National Risk Index, USGS Natural Hazards composite. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètreslocation· string · requis
US city and state (e.g. Miami FL or Houston Texas)
Exemple d'appel{"name": "get_climate_risk_score", "arguments": {"location": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cms_facility_benchmark≈ 224 tokens
Use when benchmarking hospital operating costs against CMS peer cohort or preparing a healthcare CFO board presentation. Returns peer_group context, benchmark_percentiles, metadata, source attribution, and optional database_row detail from the matched CMS benchmark row by bed size, state, and hospital type. Example: 300-bed acute care hospital in Illinois — peer group and percentile outputs show where operating metrics sit versus cohort benchmarks. Source: CMS HCRIS cost reports. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresstate· string · requisbed_size· number · requishospital_type· string · optionnel
Exemple d'appel{"name": "get_cms_facility_benchmark", "arguments": {"state": "texte", "bed_size": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cms_open_payments_profile≈ 232 tokens
Use when assessing physician payment transparency risk, evaluating manufacturer relationships, or preparing Sunshine Act compliance reporting. Returns CMS Open Payments aggregates by physician or manufacturer — payment amounts, types, and program year breakdown. Example: Dr. Smith — $847K general payments from 3 manufacturers in 2022, 67% from one device company in consulting fees — concentration above $100K triggers enhanced compliance review. Source: CMS Open Payments Sunshine Act database. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresprogram_year· number · optionnelrecipient_name· string · requismanufacturer_name· string · optionnel
Exemple d'appel{"name": "get_cms_open_payments_profile", "arguments": {"recipient_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cms_star_rating≈ 214 tokens
Use when advising on CMS Hospital Star Rating strategy or benchmarking a hospital quality performance trajectory. Returns domain weights, national distribution benchmarks, and improvement priorities. Example: Mortality domain weighted at 22% of overall star — hospitals moving 3 to 4 stars typically require 18-month mortality improvement program — 3-star hospitals represent 41% of the national distribution. Source: CMS Care Compare methodology. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresstate· string · optionnelhospital_name· string · optionnelcurrent_star_rating· number · optionnel
Exemple d'appel{"name": "get_cms_star_rating", "arguments": {"state": "texte", "hospital_name": "texte", "current_star_rating": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_colorado_ai_act_requirements≈ 306 tokens
Use when building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements. Colorado SB 205 takes effect June 30, 2026 — the first comprehensive US state AI law. Returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure ($20,000 per violation, AG enforcement), and comparison to EU AI Act. Example: AI-based loan underwriting system deployed in Colorado requires algorithmic impact assessment, plain-language consumer disclosure before first use, 3-year audit trail with AG access rights, and annual compliance certification — noncompliance triggers $20,000 per violation. Source: Colorado SB 205, enacted May 17, 2024. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètressystem_type· string · optionnel
Type of AI system (e.g. hiring, lending, healthcare, insurance, education) for tailored obligation analysis
Exemple d'appel{"name": "get_colorado_ai_act_requirements", "arguments": {"system_type": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_commodity_benchmark≈ 204 tokens
Live commodity price benchmarks — WTI crude, natural gas, gold, copper, wheat, soybeans. Weekly and monthly price changes, inflation pressure signal. Source: FRED. Updated daily. For traders and macro analysts. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètrescategory· string · optionnel · energy | metals | agriculture | all
Exemple d'appel{"name": "get_commodity_benchmark", "arguments": {"category": "energy"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_company_intelligence_synthesis≈ 153 tokens
Live company intelligence synthesis: recent litigation, regulatory exposure, financial and news developments for a named company with ML-DSA-65 signed receipt for due diligence workflows. $1.00 USDC per call.
Paramètres, retour & exemple (2)
Paramètresfocus· string · optionnelcompany· string · requis
Exemple d'appel{"name": "get_company_intelligence_synthesis", "arguments": {"company": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_company_salary_disclosure≈ 232 tokens
Use when benchmarking compensation against disclosed employer wages or assessing H-1B wage practices before a talent acquisition or competitive hire. Returns DOL LCA and H-1B wage aggregates by employer, job title, state, and fiscal year. Example: Microsoft H-1B software engineer — prevailing wage Level III $178K in Seattle, Level IV $215K — 847 certified positions in 2023, concentrated in Washington and California. Source: DOL Office of Foreign Labor Certification public filings. $0.02 USDC per call.
Paramètres, retour & exemple (4)
Paramètresstate· string · optionneljob_title· string · optionnelfiscal_year· number · optionnelcompany_name· string · requis
Exemple d'appel{"name": "get_company_salary_disclosure", "arguments": {"company_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_competitive_displacement_signal≈ 201 tokens
Use when tracking competitive threats to an incumbent vendor or identifying switching trends in a software category. Returns vendors mentioned as replacements for a target vendor with switch narrative and mention counts. Example: Salesforce displacement — HubSpot replacing in SMB at 28 mentions, Dynamics replacing in enterprise at 19 — highest displacement pressure in mid-market 100-500 employees. Source: Stratalize citation displacement composite. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètresvendor_name· string · requis
Exemple d'appel{"name": "get_competitive_displacement_signal", "arguments": {"vendor_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_competitive_landscape_synthesis≈ 135 tokens
Competitive landscape synthesis for a market category: current leaders, positioning, and dynamics synthesized from live web sources with a signed provenance receipt. $1.00 USDC per call.
Paramètres, retour & exemple (1)
Paramètresmarket· string · requis
Exemple d'appel{"name": "get_competitive_landscape_synthesis", "arguments": {"market": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_construction_cost_benchmark≈ 250 tokens
Construction cost benchmarks — hard cost per SF by building type and region, soft cost ratios, contingency standards, and live material cost escalation signals. Sources: NAHB, Turner Building Cost Index, RSMeans composites. For developers, lenders, and project owners. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresregion· string · optionnel · national | northeast | southeast | midwest | southwest | west_coastbuilding_type· string · requis · single_family | multifamily_wood_frame | multifamily_concrete | office_class_a | office_class_b | industrial_warehouse | retail_strip | medical_officeconstruction_class· string · optionnel · A | B | C
Exemple d'appel{"name": "get_construction_cost_benchmark", "arguments": {"building_type": "single_family"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_consumer_sentiment_benchmark≈ 206 tokens
Live consumer sentiment benchmarks from FRED — University of Michigan sentiment, Conference Board confidence, retail sales, PCE, personal saving rate. Strong/moderate/weak consumer signal for GDP and equity agents. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètresfocus· string · optionnel · sentiment | spending | saving | all
Exemple d'appel{"name": "get_consumer_sentiment_benchmark", "arguments": {"focus": "sentiment"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_copper_price_benchmark≈ 148 tokens
IMF Grade A copper price in USD per metric ton with year-over-year change and industrial demand significance note. Use as a coincident indicator for global manufacturing and construction cycles. Source: FRED / IMF. $0.02 atomic. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_copper_price_benchmark", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_corporate_debt_benchmark≈ 268 tokens
Use when assessing a company debt capacity, benchmarking leverage against sector peers, or preparing a refinancing or credit rating discussion. Corporate leverage and debt benchmarks — Net Debt/EBITDA, interest coverage, and debt maturity profiles by credit rating tier and industry. Source: S&P Capital IQ public aggregates and Damodaran. Used by CFOs and treasurers for refinancing, covenant setting, and credit rating management. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresindustry· string · requis · technology | healthcare | financial_services | manufacturing | retail | energy | consumer_staples | industrialscredit_rating_tier· string · optionnel · investment_grade | bb | b | ccc_and_below | unrated
Exemple d'appel{"name": "get_corporate_debt_benchmark", "arguments": {"industry": "technology"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cost_plus_price≈ 256 tokens
Use when a patient, benefits manager, or procurement agent needs Mark Cuban Cost Plus Drugs transparent retail pricing for a specific NDC. Returns medication_name, brand_name, form, unit_price and unit_billing_price labeled transparent retail price estimate (cost+15% model), optional quantity quote, and canonical purchase URL — never labeled as a benchmark. Example: NDC 42385096230 — unit price $0.963 transparent retail price estimate (cost+15% model). Source: Cost Plus Drugs public API. | x402 SLA: $0.02 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_sources[] discloses estimate provenance bound by synthesis.output_hash.
Paramètres, retour & exemple (2)
Paramètresndc_or_name· string · requisquantity_units· number · optionnel
Exemple d'appel{"name": "get_cost_plus_price", "arguments": {"ndc_or_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cra_performance_ratings≈ 255 tokens
Use when evaluating a bank's Community Reinvestment Act track record before a merger application, charter acquisition, branch expansion approval, or community lending partnership. CRA ratings — Outstanding, Satisfactory, Needs to Improve, Substantial Noncompliance — are a primary federal approval factor for bank mergers and acquisitions. A 'Needs to Improve' rating can delay or block merger approval by 12-24 months. Example: Heartland Community Bank — Outstanding CRA rating, 2023 FDIC exam, fourth consecutive Outstanding — maximum approval runway for pending acquisition of Gateway Savings Bank. Source: FFIEC CRA Ratings Database — the official federal record. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètresinstitution_name· string · requis
Exemple d'appel{"name": "get_cra_performance_ratings", "arguments": {"institution_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cre_acquisition_diligence≈ 293 tokens
Use when underwriting a CRE acquisition and need one signed composite across cap rates, CRE debt, operating costs, property tax, debt-stress, and location climate risk score. Degrades per-section on source_unavailable. Inputs location and state (required); optional asset_class, property_type, market_tier. $3.00 USDC per call.
Paramètres, retour & exemple (5)
Paramètresstate· string · requislocation· string · requisasset_class· string · optionnel · multifamily | industrial | office | retail_strip | retail_mall | hospitality | self_storage | senior_housingmarket_tier· string · optionnel · gateway | secondary | tertiaryproperty_type· string · optionnel · office_class_a | office_class_b | industrial | multifamily | retail_strip | retail_regional_mall | self_storage | hotel
Exemple d'appel{"name": "get_cre_acquisition_diligence", "arguments": {"state": "texte", "location": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_cre_debt_benchmark≈ 216 tokens
Commercial real estate debt benchmarks — DSCR minimums, LTV maximums, and spread ranges by property type and lender type (bank, agency, CMBS, life company). Source: MBA CREF databook and Trepp public data. For CRE CFOs and capital markets teams structuring financings. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètreslender_type· string · optionnel · bank | agency_fannie_freddie | cmbs | life_company | debt_fund | bridgeproperty_type· string · requis · multifamily | industrial | office | retail_strip | retail_mall | hospitality | self_storage | senior_housing
Exemple d'appel{"name": "get_cre_debt_benchmark", "arguments": {"property_type": "multifamily"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_credit_spread_benchmark≈ 205 tokens
Live investment grade and high yield credit spread benchmarks from FRED ICE BofA indices — OAS by rating tier, TED spread, 2s10s Treasury spread, and distress signal. Updates daily. For credit analysts and fixed income PMs. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètresrating_tier· string · optionnel · all | ig | hy | bbb
Exemple d'appel{"name": "get_credit_spread_benchmark", "arguments": {"rating_tier": "all"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_credit_union_benchmark≈ 182 tokens
Credit union financial performance benchmarks — capital ratios, net interest margin, loan growth, and delinquency rates by asset size. Source: NCUA quarterly call report public data. For credit union CFOs preparing for NCUA exams and board reporting. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescharter_type· string · optionnel · federal | stateasset_size_tier· string · requis · under_100m | 100m_500m | 500m_1b | over_1b
Exemple d'appel{"name": "get_credit_union_benchmark", "arguments": {"asset_size_tier": "under_100m"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_crypto_correlation_benchmark≈ 202 tokens
30-day rolling correlation matrix for BTC, ETH, and SOL — Pearson correlation pairs, beta to BTC, dominance context, and portfolio diversification signal. Source: DeFiLlama historical prices. For crypto portfolio agents. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètresperiod· string · optionnel · 7d | 30d | 90d
Exemple d'appel{"name": "get_crypto_correlation_benchmark", "arguments": {"period": "7d"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_dao_treasury_benchmark≈ 152 tokens
DAO treasury benchmarks — top DAOs by treasury size, stablecoin percentage, runway, and governance token concentration. Median benchmarks: $550M treasury, 61% stablecoin, 48-month runway. Source: DeepDAO public data. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètressort_by· string · optionnel · treasury | stablecoin_pctmin_treasury_usd· number · optionnel
Exemple d'appel{"name": "get_dao_treasury_benchmark", "arguments": {"sort_by": "treasury", "min_treasury_usd": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_data_freshness_attestation≈ 161 tokens
Use when an examiner or compliance workflow needs a post-quantum signed attestation of screening-list currency. Returns list_version and as-of metadata for synced OFAC, OIG LEIE, or SAM exclusion datasets — the same versions bound into screening responses. Input dataset: ofac, oig, or sam. $1.00 USDC per call.
Paramètres, retour & exemple (1)
Paramètresdataset· string · requis
Exemple d'appel{"name": "get_data_freshness_attestation", "arguments": {"dataset": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_defi_yield_benchmark≈ 206 tokens
DeFi lending and stable yield benchmark from DeFiLlama Yields — top pools by APY with p25/p50/p75 APY bands, TVL, chain, and pool id. Optional protocol (project slug substring) and/or asset (symbol substring). With no filters, universe is stablecoin-marked pools (typical lending / money-market supply). Free public API, no key. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresasset· string · optionnel
Filter by pool symbol substring, e.g. USDC, DAI, ETHprotocol· string · optionnel
Filter by DeFiLlama project slug substring, e.g. aave-v3, compound-v3
Exemple d'appel{"name": "get_defi_yield_benchmark", "arguments": {"asset": "texte", "protocol": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_development_pro_forma_benchmark≈ 210 tokens
Development pro forma benchmarks — yield on cost, profit-on-cost, construction-to-perm spread, and return hurdles by product type. For developers underwriting new projects and lenders sizing construction loans. Sources: NAHB, ULI, industry composite. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresmarket_tier· string · optionnel · gateway | secondary | tertiaryproduct_type· string · requis · multifamily_market_rate | multifamily_affordable | single_family_build_to_rent | industrial | office | retail | mixed_use | self_storage
Exemple d'appel{"name": "get_development_pro_forma_benchmark", "arguments": {"product_type": "multifamily_market_rate"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_device_clearances≈ 183 tokens
FDA 510(k) device clearance history from OpenFDA by device name. Returns K numbers, applicants, decisions, and receipt dates. Use for medtech competitive intelligence, regulatory pathway research, and supplier qualification. Source: FDA 510(k) database. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
Paramètres, retour & exemple (1)
Paramètresdevice_name· string · requis
Exemple d'appel{"name": "get_device_clearances", "arguments": {"device_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_docket_intelligence≈ 178 tokens
Active federal dockets for a named party — open cases only, last filing date, nature of suit, and most active courts. Source: CourtListener. Use for ongoing litigation monitoring. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.10 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescourt· string · optionnelparty_name· string · requis
Exemple d'appel{"name": "get_docket_intelligence", "arguments": {"party_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_dol_labor_violations≈ 269 tokens
Use when screening an employer, vendor, or acquisition target for wage and hour compliance risk before a contract award, supply chain partnership, PE acquisition, or HR due diligence review. Returns DOL Wage and Hour Division enforcement history — FLSA overtime violations, minimum wage violations, child labor violations — with back wages assessed and employees affected. Repeat violations are a strong predictor of class action exposure. Example: Logistics Co LLC — 3 WHD investigations 2019-2023, $1.2M back wages, 891 employees affected for FLSA overtime violations — classified repeat violator, 340% higher class action probability vs first-time violators. Source: DOL WHISARD Enforcement Database. $0.10 USDC per call.
Paramètres, retour & exemple (2)
Paramètresstate· string · optionnelemployer_name· string · requis
Exemple d'appel{"name": "get_dol_labor_violations", "arguments": {"employer_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_drug_adverse_events≈ 200 tokens
FAERS drug adverse event reports from OpenFDA by medicinal product name. Returns serious event counts, reactions, outcomes, and recent report chronology. Use for pharmacovigilance monitoring, safety signal detection, and clinical risk agents. Source: FDA FAERS. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
Paramètres, retour & exemple (2)
Paramètreslimit· integer · optionneldrug_name· string · requis
Exemple d'appel{"name": "get_drug_adverse_events", "arguments": {"drug_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_drug_label_intelligence≈ 185 tokens
FDA drug label intelligence from OpenFDA DailyMed extracts. Returns brand and generic names, manufacturer, route, indications summary, and warnings summary. Use for formulary review, pharmacology research, and adverse event context. Source: FDA drug labels. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
Paramètres, retour & exemple (1)
Paramètresdrug_name· string · requis
Exemple d'appel{"name": "get_drug_label_intelligence", "arguments": {"drug_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_drug_recall_status≈ 234 tokens
Use when a pharmacy, supply chain, or compliance agent needs FDA drug recall status for an NDC or drug name. Returns recall records (status, classification, reason, dates), match_confidence (ndc_exact or name_match), normalized ndc11, and openFDA provenance — never silently returning a different product's recalls. Example: NDC 41163-703-10 — Class II nasal spray recall records with termination dates. Source: openFDA drug/ndc and drug/enforcement. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_sources[] discloses provenance bound by synthesis.output_hash.
Paramètres, retour & exemple (1)
Paramètresndc_or_name· string · requis
Exemple d'appel{"name": "get_drug_recall_status", "arguments": {"ndc_or_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_earnings_quality_benchmark≈ 233 tokens
Earnings quality and financial statement risk benchmarks — accruals ratio, cash conversion, and revenue recognition risk by sector. Source: SEC EDGAR aggregate + Sloan accruals model (academic standard). For CFOs, auditors, and analysts assessing financial reporting risk before M&A or investment. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètressector· string · requis · technology | healthcare | financial_services | consumer_staples | energy | industrials | real_estate | utilitiesrevenue_recognition_model· string · optionnel · subscription | project_based | product_sales | financial_services | mixed
Exemple d'appel{"name": "get_earnings_quality_benchmark", "arguments": {"sector": "technology"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_ehr_cost_per_bed≈ 212 tokens
Use when benchmarking EHR maintenance costs before a contract renewal or evaluating health IT budget efficiency. Returns benchmark cost per licensed bed with optional gap analysis when actual cost and bed count are provided. Example: Epic maintenance median $4,500/bed — 300-bed hospital at $6,200/bed is 38% above market — renegotiation trigger especially strong at 5+ year renewal cycles. Source: KLAS 2024, Kaufman Hall EHR TCO composite. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresbed_count· number · optionnelannual_cost· number · optionnelvendor_name· string · requis
Exemple d'appel{"name": "get_ehr_cost_per_bed", "arguments": {"vendor_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_eia_energy_public_snapshot≈ 172 tokens
Use when current energy price data is needed for a commodity brief, input cost analysis, or energy sector context in a CFO or investment brief. Returns WTI crude and natural gas spot prices when EIA API is configured. Example: WTI crude $78.40/bbl, natural gas $2.31/MMBtu — energy input costs 12% below year-ago levels, favorable for manufacturing and transportation operating margins. Source: US Energy Information Administration. $0.02 USDC per call.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_eia_energy_public_snapshot", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_elliott_waves≈ 158 tokens
Use when a technical trader needs wave counts, targets, and invalidation levels for major assets. Returns wave position, degree, target high/low, invalidation, and confidence for BTC, SPY, TLT, Gold. Example: wave label, target band, invalidation, and confidence score per asset. $0.50 USDC per call.
Paramètres, retour & exemple (1)
Paramètresasset· string · optionnel
Asset symbol or "all" (default all)
Exemple d'appel{"name": "get_elliott_waves", "arguments": {"asset": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_employer_h1b_wages≈ 214 tokens
Use when analyzing an employer H-1B compensation strategy or benchmarking tech sector wages against DOL prevailing wage data. Returns prevailing wage statistics, certified job titles, wage levels, and state distribution from DOL LCA filings. Example: Google H-1B — software engineer Level IV prevailing wage $195K, 1,243 certified positions in 2023 — concentrated in Mountain View and New York City offices. Source: DOL Labor Condition Application public data. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètresemployer_name· string · requis
e.g. Google, Deloitte, Cognizant
Exemple d'appel{"name": "get_employer_h1b_wages", "arguments": {"employer_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_employment_cost_index≈ 142 tokens
BLS Employment Cost Index year-over-year change for total compensation, wages, and benefits. Use when modeling labor cost inflation, contract escalation, and margin pressure in operating plans. Source: BLS ECI. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_employment_cost_index", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_esg_benchmark≈ 178 tokens
ESG benchmarks by sector — carbon intensity Scope 1/2, net zero commitments, SBTi alignment, board independence, pay equity, and ESG composite scores. Sources: EPA GHGRP, MSCI ESG methodology. For sustainability agents and ESG analysts. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresfocus· string · optionnel · carbon | social | governance | allsector· string · optionnel · technology | energy | financial_services | healthcare | manufacturing | all
Exemple d'appel{"name": "get_esg_benchmark", "arguments": {"focus": "carbon", "sector": "technology"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_eu_ai_act_coverage≈ 224 tokens
Use when assessing EU AI Act compliance readiness ahead of the August 2, 2026 enforcement deadline or preparing a board AI governance briefing. Returns a composite payload with framework, deadline, total_controls, controls[], hint, and query timestamp, optionally filtered by NIST function from compliance_controls reference data. Example: Filter by MAP to review mapped EU AI Act controls and implementation statuses in the returned controls array for governance planning. Source: EU AI Act mappings in compliance_controls reference data. $0.50 USDC per call.
Paramètres, retour & exemple (1)
ParamètresnistFunction· string · optionnel · GOVERN | MAP | MEASURE | MANAGE
Exemple d'appel{"name": "get_eu_ai_act_coverage", "arguments": {"nistFunction": "GOVERN"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_fda_recall_history≈ 280 tokens
Use when evaluating a pharmaceutical company, medical device manufacturer, or healthcare vendor for product safety risk, supply chain exposure, or regulatory compliance standing. Returns FDA recall classifications (Class I = risk of serious harm, Class II = moderate risk, Class III = unlikely to cause harm) with product descriptions and recall reasons. Class I recalls trigger mandatory FDA press releases and procurement review obligations. Example: MedSupply Corp — 2 Class I drug recalls in 36 months: contaminated IV solutions (2022) and mislabeled injectable (2023) — pattern of serious quality control failures requiring immediate vendor review. Source: OpenFDA Enforcement Reports. $0.10 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescompany_name· string · requisproduct_type· string · optionnel · drug | device | both
Exemple d'appel{"name": "get_fda_recall_history", "arguments": {"company_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_fec_campaign_finance≈ 171 tokens
Federal campaign finance activity — PAC committees, total political disbursements, receipts, and political footprint signal. Source: FEC electronic filings. Use for political risk monitoring and PAC compliance. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètresname· string · requis
Exemple d'appel{"name": "get_fec_campaign_finance", "arguments": {"name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_federal_case_law≈ 174 tokens
Federal court opinion search — keyword or party name search with citation counts, court, date, and excerpt. Optional court filter (e.g. scotus, ca2, dcd). Source: CourtListener. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.10 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescourt· string · optionnelquery· string · requis
Exemple d'appel{"name": "get_federal_case_law", "arguments": {"query": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_federal_contract_intelligence≈ 259 tokens
Use when researching a company federal revenue concentration, identifying government contract competitors, or assessing vendor dependency on federal business. Returns contract obligation data by vendor, agency, NAICS code, and fiscal year from USASpending. Example: Acme IT Services — $847M federal obligations FY2023, 67% from DoD, 3 agencies representing 89% of revenue — high concentration risk for supply chain or M&A due diligence. Source: USASpending.gov synced data. $0.02 USDC per call.
Paramètres, retour & exemple (5)
Paramètresstate· string · optionnel
Two-letter state code for place of performance (e.g. PA, IL).naics_code· string · optionnelagency_name· string · optionnelfiscal_year· number · optionnelvendor_name· string · requis
Exemple d'appel{"name": "get_federal_contract_intelligence", "arguments": {"vendor_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_federal_court_cases≈ 306 tokens
Use when screening a company, executive, vendor, or counterparty for federal litigation exposure before a contract award, acquisition, investment, board appointment, or enterprise partnership. Returns active and historical federal court dockets across all US district and appellate courts — case names, docket numbers, courts, filing dates, nature of suit, and active status. Example: Acme Corp — 4 active federal cases: patent infringement N.D. Cal. (filed 2023), FLSA collective action S.D.N.Y. with 847 plaintiffs (filed 2023), FTC antitrust investigation D.D.C. (filed 2024), securities class action S.D.N.Y. (filed 2024) — aggregate litigation liability exposure estimated above $200M. Source: CourtListener, 1M+ federal court documents. $0.50 USDC per call.
Paramètres, retour & exemple (3)
Paramètrescourt· string · optionnel
Court identifier e.g. ca9, scotus, dcd, nyed, ndcaparty_name· string · requisyears_back· number · optionnel
Exemple d'appel{"name": "get_federal_court_cases", "arguments": {"party_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_financial_deal_readiness≈ 308 tokens
Use when assessing financial health and deal-readiness for M&A, lending, or investment committee review. Fans in earnings quality, working capital, WACC, corporate debt, credit spread, and ESG benchmarks — degrading per-section on source_unavailable. Input sector (required); optional industry, credit_rating_tier, credit_spread_tier, esg_focus. $3.00 USDC per call.
Paramètres, retour & exemple (5)
Paramètressector· string · requis · technology | healthcare | financial_services | consumer_staples | energy | industrials | real_estate | utilitiesindustry· string · optionnel · saas | healthcare | legal | manufacturing | retail | financial_servicesesg_focus· string · optionnel · carbon | social | governance | allcredit_rating_tier· string · optionnel · investment_grade | bb | b | ccc_and_below | unratedcredit_spread_tier· string · optionnel · all | ig | hy | bbb
Exemple d'appel{"name": "get_financial_deal_readiness", "arguments": {"sector": "technology"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_fomc_rate_probability≈ 177 tokens
Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_fomc_rate_probability", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_form4_insider_cluster≈ 158 tokens
Use when detecting insider trading clusters from Form 4 filing density over a window. Returns form4_filing_count, cluster_score, and activity signal for a ticker. Source: SEC EDGAR Form 4 cluster analysis. $0.50 USDC per call.
Paramètres, retour & exemple (2)
Paramètresticker· string · requisdays_back· integer · optionnel
Exemple d'appel{"name": "get_form4_insider_cluster", "arguments": {"ticker": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_form_d_new_raises≈ 182 tokens
Use when tracking new private placement activity via SEC Form D filings in a recent window. Returns Form D filings from EDGAR full-text search with optional state and SIC filters. Source: SEC EDGAR EFTS. $0.10 USDC per call.
Paramètres, retour & exemple (4)
Paramètressic· string · optionnelstate· string · optionneldays_back· integer · requismin_offering_usd· number · optionnel
Exemple d'appel{"name": "get_form_d_new_raises", "arguments": {"days_back": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_ftc_enforcement_history≈ 240 tokens
Use when evaluating antitrust exposure, consumer protection liability, data privacy enforcement history, or deceptive practices risk for a company before an acquisition, strategic partnership, or enterprise vendor selection. FTC consent orders impose ongoing behavioral restrictions lasting 10-20 years and carry $50,000+ per day penalties for violations. Example: Tech Platform Corp — FTC consent order 2021, $150M civil penalty, 20-year restrictions on data monetization practices, biennial compliance reporting — restrictions survive acquisition and bind acquirer. Source: FTC Enforcement Cases and Proceedings. $0.50 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescompany_name· string · requis
Exemple d'appel{"name": "get_ftc_enforcement_history", "arguments": {"company_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_fx_rate_benchmark≈ 194 tokens
Live major currency pair benchmarks — USD/EUR, USD/JPY, USD/GBP, USD/CNY, USD/CAD, USD/MXN, DXY broad TWI, carry trade spread, and weekly/monthly/YTD rate change. Source: FRED. Updated daily. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètresbase_currency· string · optionnel · USD | EUR | GBP
Exemple d'appel{"name": "get_fx_rate_benchmark", "arguments": {"base_currency": "USD"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_gas_benchmark≈ 183 tokens
Live gas price benchmarks for Ethereum, Base, and Solana. Returns Gwei, USD cost per transfer type, congestion category, and x402 agent economy context. Base vs ETH savings comparison. Source: public chain RPCs. Zero API key required. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètreschain· string · optionnel · ethereum | base | solana | all
Exemple d'appel{"name": "get_gas_benchmark", "arguments": {"chain": "ethereum"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_github_ecosystem_intelligence≈ 210 tokens
Use when assessing a technology vendor open-source presence, evaluating developer community strength, or researching a company GitHub footprint before a technical due diligence. Returns organization profile and top repository stats — stars, forks, contributors, and language breakdown. Example: HashiCorp GitHub — 18 public repos, Terraform at 38,000 stars, 147,000 forks, 2,800 contributors — strong community signal supporting enterprise adoption thesis. Source: GitHub public API. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètresorg_or_company· string · requis
Exemple d'appel{"name": "get_github_ecosystem_intelligence", "arguments": {"org_or_company": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_global_equity_benchmark≈ 202 tokens
Global equity index benchmarks — S&P 500, Nasdaq, Russell 2000, Stoxx 600, DAX, FTSE 100, Nikkei 225, Hang Seng, Shanghai Composite, MSCI EM. YTD returns, P/E ratios, and risk-on/risk-off global signal. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètresregion· string · optionnel · us | europe | asia | emerging | all
Exemple d'appel{"name": "get_global_equity_benchmark", "arguments": {"region": "us"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_gpo_contract_benchmark≈ 187 tokens
Use when benchmarking GPO contract performance or building a supply chain cost reduction case for a hospital board. Returns typical GPO savings percentage, leakage rate, and top savings categories. Example: Acute care GPO median savings 18% vs non-contract pricing — leakage rate 22% means 1-in-5 purchases bypass contract — leakage above 30% triggers mandatory compliance programs at most health systems. Source: HFMA and CMS composite. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescategory· string · optionnel
Exemple d'appel{"name": "get_gpo_contract_benchmark", "arguments": {"category": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_healthcare_category_intelligence≈ 205 tokens
Use when researching which vendors dominate AI recommendations in a healthcare technology category or validating a health IT vendor selection. Returns top recommended vendors, AI consensus narrative, and sample size from healthcare-specific citation analysis. Example: EHR category — Epic leads at 67% AI citation share, Oracle Health 18%, MEDITECH 9% — consensus near-universal for large health systems, fragmenting below 200 beds. Source: Stratalize AI citation composite. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescategory· string · requis
Exemple d'appel{"name": "get_healthcare_category_intelligence", "arguments": {"category": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_healthcare_vendor_gpo_diligence≈ 242 tokens
Use when diligencing a healthcare vendor or GPO relationship. Fans in healthcare vendor market rate, GPO contract benchmark, FDA recall history, payer intelligence, and hospital supply chain benchmark — degrading per-section on source_unavailable. Inputs vendor_name and bed_size (required). $3.00 USDC per call.
Paramètres, retour & exemple (6)
Paramètresstate· string · optionnelbed_size· integer · requiscategory· string · optionnelpayer_name· string · optionnelvendor_name· string · requiscompany_name· string · optionnel
Exemple d'appel{"name": "get_healthcare_vendor_gpo_diligence", "arguments": {"bed_size": 1, "vendor_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_healthcare_vendor_market_rate≈ 204 tokens
Use when benchmarking a healthcare vendor quote or preparing a supply chain contract negotiation. Returns market rates for EHR, staffing, food service, waste management, and med-surg by facility type. Example: Healthcare food service median $18.40/patient day for acute care — facilities above $22/patient day are 20% above market — GPO competitive rebid typically recovers 8-12%. Source: CMS and Stratalize healthcare vendor composite. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescategory· string · optionnelvendor_name· string · requis
Exemple d'appel{"name": "get_healthcare_vendor_market_rate", "arguments": {"vendor_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_hospital_care_compare_quality≈ 215 tokens
Use when evaluating hospital quality for a referral network decision, acquisition target assessment, or competitive quality analysis. Returns CMS Hospital Compare scores — safety, readmissions, patient experience, mortality, and overall star rating by hospital. Example: Northwestern Memorial — 5-star overall, top decile on mortality and safety, HCAHPS 87th percentile — benchmark for quality-driven referral network design. Source: CMS Care Compare synced data. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètreshospital_name· string · requis
Hospital or facility name
Exemple d'appel{"name": "get_hospital_care_compare_quality", "arguments": {"hospital_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_hospital_supply_chain_benchmark≈ 203 tokens
Use when benchmarking hospital supply chain efficiency against CMS peer cohort or building a materials management cost reduction case. Returns supply cost as percentage of operating expense at p25/p50/p75 by bed size and state. Example: 200-bed community hospital — supply cost 19.4% of operating expense vs 16.8% peer median — closing the gap to median recovers $2.6M annually at $130M operating budget. Source: CMS HCRIS cost reports. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresstate· string · optionnelbed_size· number · requis
Exemple d'appel{"name": "get_hospital_supply_chain_benchmark", "arguments": {"bed_size": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_housing_supply_benchmark≈ 174 tokens
Live housing supply indicators — starts, permits, completions, and absorption by market tier from FRED and Census. Leading indicator for housing prices 6-12 months ahead. For developers, lenders, investors, and housing policy analysts. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresregion· string · optionnel · national | northeast | south | midwest | weststructure_type· string · optionnel · single_family | multifamily | all
Exemple d'appel{"name": "get_housing_supply_benchmark", "arguments": {"region": "national", "structure_type": "single_family"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_hud_fair_market_rent≈ 169 tokens
HUD Fair Market Rents by metro area and bedroom count. Used for affordable housing underwriting, Section 8 Housing Choice Voucher compliance, LIHTC income limit calculations, and housing authority budgeting. Source: HUD annual FMR dataset. Free.
Paramètres, retour & exemple (2)
Paramètresmetro_area· string · requis
e.g. Chicago, IL or Miami, FLbedroom_count· integer · optionnel
Exemple d'appel{"name": "get_hud_fair_market_rent", "arguments": {"metro_area": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_imf_weo_macro_snapshot≈ 188 tokens
Use when providing global macro context for an international expansion brief, country risk assessment, or board-level economic outlook presentation. Returns IMF WEO macro composites — GDP growth, inflation, and current account balance by country group. Example: Emerging market composite — GDP growth 4.2% vs advanced economy 1.7%, inflation diverging at 7.8% — growth premium exists but requires currency and political risk premium in discount rate. Source: IMF WEO static composite, semi-annual update. $0.02 USDC per call.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_imf_weo_macro_snapshot", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_industry_spend_benchmark≈ 214 tokens
Use when validating total IT spend against industry peers or building a software budget baseline for a CFO board presentation. Returns median monthly total software stack spend, category breakdown, and productivity tool medians by industry. Example: Mid-market healthcare org — median total SaaS spend $18,500/mo, EHR and clinical tools 41% of stack, productivity suite $2,800/mo — organizations above $26,000/mo are consolidation candidates. Source: Stratalize industry composite. $0.10 USDC per call.
Paramètres, retour & exemple (2)
Paramètresindustry· string · requiscompany_size· string · optionnel
Exemple d'appel{"name": "get_industry_spend_benchmark", "arguments": {"industry": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_industry_spend_profile≈ 248 tokens
Use when sizing a technology budget for a specific industry and headcount, or identifying category spend outliers. Returns spend bands, category ranges, and outlier flags scaled to employee count. Example: 500-person healthcare org — total SaaS stack median $1.2M/yr, EHR 34% of spend, clinical productivity tools 18% — organizations above $1.8M are consolidation candidates. Source: Stratalize workforce-scaled composite. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresindustry· string · requis · saas | healthcare | legal | manufacturing | retail | financial_services
Industry verticalemployee_count· number · requis
Employee headcount for banding
Exemple d'appel{"name": "get_industry_spend_profile", "arguments": {"industry": "saas", "employee_count": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_inflation_benchmark≈ 207 tokens
Live inflation benchmarks from FRED — CPI, core CPI, PCE, core PCE, 5Y and 10Y TIPS breakeven expectations, shelter and medical care components. Fed target gap, anchoring signal, and policy implication for macro agents. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètresmeasure· string · optionnel · cpi | pce | breakeven | components | all
Exemple d'appel{"name": "get_inflation_benchmark", "arguments": {"measure": "cpi"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_insurance_benchmark≈ 200 tokens
Insurance financial performance benchmarks — combined ratio, loss ratio, expense ratio, and reserve adequacy by line of business. Source: NAIC annual statistical report. For insurance CFOs, actuaries, and analysts reviewing underwriting performance. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètrescompany_size· string · optionnel · small | mid_size | largeline_of_business· string · requis · property_casualty | commercial_liability | workers_comp | auto | homeowners | life | health | specialty
Exemple d'appel{"name": "get_insurance_benchmark", "arguments": {"line_of_business": "property_casualty"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_intel_contractor_profile≈ 223 tokens
Use when vetting a contractor or bidder before a teaming decision or subcontract award. Returns the entity record plus award and bid participation history by role (winner, bidder, or vehicle holder). Example: Acme Paving LLC, winner on 14 of 61 bids (23% win rate), low bidder on 9 of those 14: a thin win margin worth checking against recent unit-price trends before teaming. Source: l. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescontractor· string · requis
UEI, legal name, or entity id
Exemple d'appel{"name": "get_intel_contractor_profile", "arguments": {"contractor": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_intel_findings_search≈ 270 tokens
Use when researching tribunal precedent for a specific defect or claim type before filing or defending a construction claim. Returns findings filtered by defect family, claim type, outcome, owner, and keyword, with docket number and case name from the underlying decision. Example: differing-site-conditions findings against notice-and-time-bar defenses, 31% denial rate across 58 findi. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (6)
Paramètreslimit· number · optionnelkeyword· string · optionnel
Matched against the finding summaryoutcome· string · optionnel
granted, denied, partial, or remandedowner_id· string · optionnelclaim_type· string · optionneldefect_family· string · optionnel
Exemple d'appel{"name": "get_intel_findings_search", "arguments": {"limit": 1, "keyword": "texte", "outcome": "texte", "owner_id": "texte", "claim_type": "texte", "defect_family": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_intel_join≈ 230 tokens
Use when researching a contractor's track record with a specific owner before a bid or teaming decision. Returns awards where that contractor (by UEI) participated under that owner plus the owner's general tribunal-decision findings for context. Example: USACE Norfolk District and a mid-Atlantic marine contractor, 6 joint awards worth $84M, set against the district's 42 findings and. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresuei· string · requis
Contractor UEIowner· string · requis
Owner slug or id
Exemple d'appel{"name": "get_intel_join", "arguments": {"uei": "texte", "owner": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_intel_lettings_search≈ 297 tokens
Use when scoping upcoming or recent construction lettings in a target geography before allocating bid and estimating resources. Returns bid tabulation line items filtered by owner, county, district, state, and letting date range. Example: Cook County, IL lettings in the last 90 days: 23 lettings, 340 bid items, concentrated in District 1 resurfacing work, a near-term bidding window w. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (7)
Paramètreslimit· number · optionnelowner· string · optionnel
Owner slug or idstate· string · optionnel
Two-letter state code, e.g. IL, TXcounty· string · optionneldate_to· string · optionnel
Letting date range end, YYYY-MM-DDdistrict· string · optionneldate_from· string · optionnel
Letting date range start, YYYY-MM-DD
Exemple d'appel{"name": "get_intel_lettings_search", "arguments": {"limit": 1, "owner": "texte", "state": "texte", "county": "texte", "date_to": "texte", "district": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_intel_owner_profile≈ 220 tokens
Use when assessing a construction owner's claims history before bidding a contract or negotiating a change order. Returns aggregate finding counts by defect family and outcome, decisions in the corpus, and days and dollars claimed versus awarded. Example: USACE Norfolk District, 42 findings, 31% denial rate on notice-and-time-bar defenses, $22M claimed versus $9M awarded: a hard line. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètresowner· string · requis
Owner slug or id, e.g. illinois-dot
Exemple d'appel{"name": "get_intel_owner_profile", "arguments": {"owner": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_intel_unit_prices≈ 254 tokens
Use when benchmarking a bid line-item price against recent public lettings before finalizing an estimate. Returns minimum, maximum, average, and sample count unit prices for a pay item code, filterable by owner, state, and county. Example: pay item 20101100 (clearing and grubbing) in Texas, average $2,340 per acre across 46 bid items, range $1,180 to $4,900: a wide spread that flags. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.02 USDC per call.
Paramètres, retour & exemple (4)
Paramètresowner· string · optionnel
Owner slug or idstate· string · optionnel
Two-letter state code, e.g. IL, TXcounty· string · optionnelpay_item_code· string · requis
Exemple d'appel{"name": "get_intel_unit_prices", "arguments": {"pay_item_code": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_investment_category_signal≈ 193 tokens
Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescategory· string · requis
Exemple d'appel{"name": "get_investment_category_signal", "arguments": {"category": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_irs_990_intelligence≈ 192 tokens
IRS Form 990 nonprofit financial data — total revenue, expenses, net assets, program expense ratio, executive compensation, revenue trend, and financial health signal. Source: ProPublica Nonprofit Explorer. Essential for evaluating nonprofit health systems, universities, and foundations. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètresorg_name· string · requis
Exemple d'appel{"name": "get_irs_990_intelligence", "arguments": {"org_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_irs_industry_tax_statistics≈ 336 tokens
Use when benchmarking financial performance against industry-level tax return data, establishing valuation comparables for M&A, assessing typical effective tax rates by sector, or producing financial due diligence context from the most authoritative source of actual US business financial performance. Returns IRS SOI aggregate statistics from actual filed corporation income tax returns — gross receipts, net income margins, and effective tax rates by industry. Data reflects actual filed returns, not survey estimates. Example: Healthcare and Social Assistance — 284,000 returns, 8.3% net income margin, 19.1% effective tax rate, $4.2M average gross receipts per return — baseline for healthcare PE valuation and acquisition multiples analysis. Source: IRS Statistics of Income Division. $0.50 USDC per call.
Paramètres, retour & exemple (2)
Paramètresindustry· string · requis
Industry name or NAICS sector (e.g. healthcare, construction, professional services, manufacturing)entity_type· string · optionnel · corporation | all
Exemple d'appel{"name": "get_irs_industry_tax_statistics", "arguments": {"industry": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_job_openings_intelligence≈ 148 tokens
JOLTS labor market intelligence from BLS: job openings, quits rate, layoffs rate, and tight/loose/normal interpretation. Use for workforce planning, wage pressure forecasting, and recession early-warning agents. Source: BLS JOLTS. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_job_openings_intelligence", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_judge_analytics≈ 177 tokens
Federal judge profile — court positions, appointment history, political affiliation, education, and opinion count. Source: CourtListener (Free Law Project). Use when assessing litigation risk or evaluating judicial philosophy. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify. $0.50 USDC per call.
Paramètres, retour & exemple (1)
Paramètresjudge_name· string · requis
Exemple d'appel{"name": "get_judge_analytics", "arguments": {"judge_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_labor_market_benchmark≈ 208 tokens
Live labor market benchmarks from FRED — unemployment, U-6 underemployment, JOLTS job openings, quit rate, labor participation, weekly claims, wage growth. Tight/balanced/loosening signal for macro agents and portfolio managers. Live source. Returns HTTP 503 (no charge) if upstream source unavailable for >50% of fields. | x402 SLA: $0.10 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_source field discloses provenance (fred_api/fred_csv/fred_mixed).
Paramètres, retour & exemple (1)
Paramètresfocus· string · optionnel · employment | wages | openings | all
Exemple d'appel{"name": "get_labor_market_benchmark", "arguments": {"focus": "employment"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_legislative_intelligence_synthesis≈ 152 tokens
Legislative intelligence synthesis: current bill status, recent passages, and amendments for a topic and jurisdiction, synthesized from live sources with a cryptographic receipt. $1.00 USDC per call.
Paramètres, retour & exemple (2)
Paramètrestopic· string · requisjurisdiction· string · optionnel
Exemple d'appel{"name": "get_legislative_intelligence_synthesis", "arguments": {"topic": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_litigation_enforcement_snapshot≈ 232 tokens
Use when assessing litigation and enforcement exposure for a counterparty. Fans in litigation risk composite, new litigation alerts, federal court cases, FTC enforcement history, and OCC enforcement actions — degrading per-section (including CourtListener-dependent alerts) on source_unavailable. Input party_name (required). $3.00 USDC per call.
Paramètres, retour & exemple (5)
Paramètrescourt· string · optionneldays_back· integer · optionnelparty_name· string · requiscompany_name· string · optionnelinstitution_name· string · optionnel
Exemple d'appel{"name": "get_litigation_enforcement_snapshot", "arguments": {"party_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_litigation_risk_composite≈ 165 tokens
Use when synthesizing litigation risk from party profile, docket intelligence, and optional judge analytics. Composite degrades transparently when partial CourtListener sources fail. Source: CourtListener composite. $0.50 USDC per call.
Paramètres, retour & exemple (2)
Paramètresjudge_name· string · optionnelparty_name· string · requis
Exemple d'appel{"name": "get_litigation_risk_composite", "arguments": {"party_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_ma_multiples_benchmark≈ 246 tokens
Use when valuing an acquisition target, benchmarking deal pricing, or preparing a fairness opinion. M&A transaction multiples — acquisition EV/EBITDA, EV/Revenue, and control premiums by industry and deal size. Source: Damodaran transaction dataset and public deal aggregates. Used by corp dev, PE deal teams, M&A advisors, and CFOs preparing fairness opinions. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresindustry· string · requis · technology | healthcare | financial_services | manufacturing | retail | energy | consumer_staples | industrialsdeal_size_tier· string · optionnel · under_50m | 50m_250m | 250m_1b | over_1b
Exemple d'appel{"name": "get_ma_multiples_benchmark", "arguments": {"industry": "technology"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_macro_market_signal≈ 182 tokens
Use when a current macro environment snapshot is needed for a trading brief, CFO board presentation, or investment committee context. Returns Fed funds rate, Treasury yields, CPI, PCE, and employment data when FRED API is configured. Example: Fed funds 5.33%, 10Y 4.42%, CPI 3.1% — rates and inflation above long-run targets, labor market tight — LATE-CYCLE positioning signal. Source: FRED St. Louis Fed, daily update. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètressignal_type· string · optionnel
Exemple d'appel{"name": "get_macro_market_signal", "arguments": {"signal_type": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_macro_playbook≈ 131 tokens
Use when a trader or portfolio manager needs current regime label and tactical positioning. Returns active regime, verifiable FOMC facts, live market snapshot, model interpretation, concurrent playbooks, and key levels. Example: regime label with playbook actions and risk triggers. $0.50 USDC per call.
Paramètres, retour & exemple (0)
Exemple d'appel{"name": "get_macro_playbook", "arguments": {}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_market_intelligence_brief≈ 203 tokens
Use when producing a quick industry intelligence brief or validating market narrative for a strategy deck. Returns AI-generated market summary, up to six key themes, and sentiment skew for any industry. Example: Healthcare IT market — POSITIVE sentiment 68%, key themes: EHR consolidation, AI-assisted coding, value-based care expansion — consolidation narrative dominant across 847 analyzed queries. Source: Stratalize AI citation composite. $0.50 USDC per call.
Paramètres, retour & exemple (2)
Paramètrestopic· string · optionnelindustry· string · requis
Exemple d'appel{"name": "get_market_intelligence_brief", "arguments": {"industry": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_market_structure_signal≈ 188 tokens
Use when assessing software category maturity, timing a market entry, or evaluating consolidation risk in a category. Returns market structure signal (consolidating or fragmenting) with citation evidence. Example: HR tech category — CONSOLIDATING signal, top 3 vendors hold 71% of AI citation share — late-stage consolidation signals pricing power shift to incumbents. Source: Stratalize market structure composite. $0.10 USDC per call.
Paramètres, retour & exemple (1)
Paramètrescategory· string · requis
Exemple d'appel{"name": "get_market_structure_signal", "arguments": {"category": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_model_risk_management_standards≈ 330 tokens
Use when preparing for a model risk management examination, building an SR 26-2 compliant model governance program, or assessing a financial institution's MRM framework against regulatory expectations. Returns Federal Reserve SR 26-2 and OCC requirements across development, independent validation, ongoing monitoring, and governance — with exam deficiency rates showing where institutions most commonly fail. For AI and ML models, SR 26-2 explicitly requires independent validation even for vendor-supplied models and black-box systems. Example: Documentation deficiencies are the most common exam finding at 67% of reviewed institutions — inadequate conceptual soundness documentation for credit scoring models triggers immediate MRA (Matter Requiring Attention). Source: Federal Reserve SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026. $0.50 USDC per call.
Paramètres, retour & exemple (1)
Paramètresinstitution_type· string · optionnel · community_bank | regional_bank | large_bank | credit_union | insurance_company | asset_manager | fintech
Exemple d'appel{"name": "get_model_risk_management_standards", "arguments": {"institution_type": "community_bank"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_mortgage_market_benchmark≈ 156 tokens
Live mortgage rate benchmarks — 30Y and 15Y fixed from FRED weekly survey, ARM spreads, points and fees, DTI standards, and affordability index. For homebuyers, lenders, real estate agents, and housing analysts. Rates update weekly. $0.02 USDC per call.
Paramètres, retour & exemple (2)
Paramètresstate· string · optionnelloan_type· string · optionnel · conventional | fha | va | jumbo
Exemple d'appel{"name": "get_mortgage_market_benchmark", "arguments": {"state": "texte", "loan_type": "conventional"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_nadac_drug_benchmark≈ 245 tokens
Use when a pharmacy buyer, 340B program manager, or CFO agent needs CMS NADAC drug acquisition cost benchmarks for contract negotiation or payer comparison. Returns latest NADAC per unit, effective_date, as_of_date, pricing_unit, ndc_description, normalized ndc11, source ndc, and restatement_detected when CMS republishes the same effective date. Example: NDC 42385096230 — NADAC $1.45422/EA as_of 2026-07-15 effective 2026-06-17. Source: CMS NADAC (data.medicaid.gov DKAN, weekly). | x402 SLA: $0.02 USDC per call. Returns HTTP 503 (no charge) when upstream data sources unavailable. data_sources[] discloses provenance string bound by synthesis.output_hash.
Paramètres, retour & exemple (1)
Paramètresndc_or_name· string · requis
Exemple d'appel{"name": "get_nadac_drug_benchmark", "arguments": {"ndc_or_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_ncreif_return_benchmark≈ 213 tokens
NCREIF Property Index institutional return benchmarks — total returns, income returns, and appreciation by property type and region. The standard benchmark for institutional real estate portfolios. Source: NCREIF quarterly public data. For pension funds, endowments, and institutional asset managers. $0.02 USDC per call.
Paramètres, retour & exemple (3)
Paramètresperiod· string · optionnel · 1y | 3y | 5y | 10yregion· string · optionnel · national | east | midwest | south | westproperty_type· string · optionnel · all_property | office | industrial | retail | apartment | hotel
Exemple d'appel{"name": "get_ncreif_return_benchmark", "arguments": {"period": "1y", "region": "national", "property_type": "all_property"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_ncua_credit_union_financials≈ 262 tokens
Use when evaluating a credit union for partnership, acquisition, membership, or competitive benchmarking in a local market. Returns NCUA call report financials — assets, deposits, loans, net worth ratio, delinquency rate, and ROA — with peer comparison signals. The same financial data NCUA examiners review during examination preparation. Well-capitalized threshold is 7% net worth ratio — institutions below this face mandatory corrective action. Example: ABC Federal Credit Union — $2.1B assets, 11.2% net worth ratio (59% above minimum), 0.38% delinquency vs 0.71% peer average — financially strong, low credit quality risk. Source: NCUA Call Report Data. $0.10 USDC per call.
Paramètres, retour & exemple (2)
Paramètresstate· string · optionnelcredit_union_name· string · requis
Exemple d'appel{"name": "get_ncua_credit_union_financials", "arguments": {"credit_union_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_new_litigation_alerts≈ 185 tokens
Use when monitoring new federal dockets filed against a party in the last N days. Returns docket alerts from CourtListener; zero results is a legitimate empty answer. Source: CourtListener docket search. $0.10 USDC per call.
Paramètres, retour & exemple (4)
Paramètrescourt· string · optionneldays_back· integer · optionnelparty_name· string · requisnature_of_suit· string · optionnel
Exemple d'appel{"name": "get_new_litigation_alerts", "arguments": {"party_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_nist_ai_rmf_requirements≈ 280 tokens
Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0. $0.50 USDC per call.
Paramètres, retour & exemple (1)
Paramètresfunction_filter· string · optionnel · GOVERN | MAP | MEASURE | MANAGE
Exemple d'appel{"name": "get_nist_ai_rmf_requirements", "arguments": {"function_filter": "GOVERN"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - get_noaa_disaster_economics≈ 246 tokens
Use when establishing the macroeconomic cost of climate risk for board-level ESG reporting, reinsurance negotiations, infrastructure investment decisions, or climate-related financial risk disclosures under SEC or TCFD frameworks. Returns NOAA's official annual billion-dollar disaster economics — event count, total losses, deaths, and historical context showing 10-year trend acceleration. Example: 2023 — 28 events, $92.9B total losses, 12% above the 10-year average — the fifth consecutive year of above-average economic losses. Cited by the Federal Reserve, Treasury, and major reinsurers as the authoritative US climate loss series. Source: NOAA NCEI. $0.02 USDC per call.
Paramètres, retour & exemple (1)
Paramètresyear· number · optionnel
Exemple d'appel{"name": "get_noaa_disaster_economics", "arguments": {"year": 1}}Exemple de retour{"content": [{"type": "text", "text": "…"}]}
+ 81 autres outils (affichage limité à 120).
C'est votre serveur ?
Revendiquez-le sur l'espace créateur (preuve GitHub, domaine ou npm) pour afficher le badge « propriétaire vérifié » et lancer un pentest comportemental à la demande afin d'obtenir le badge « Vérifié ».
Source
https://www.stratalize.com/api/mcp-public?vertical=intelligence