io.github.2016judea/small-business-intelligence

Est-ce que io.github.2016judea/small-business-intelligence est sûr ?

Au dernier audit MCP Hub, le serveur MCP « io.github.2016judea/small-business-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 16 outils

• bring_your_document≈ 977 tokens

  Reads a document the person has — a P&L, a lease, a comp set, a schedule of locations, a member list, a story's addresses, a single claim — against the Minneapolis–St. Paul public record, and returns a table that cites the file behind every cell. This is the half of the platform the public record cannot do alone: their private file, our joined records, one answer. Free, no account. The document is read in the request, answered, and dropped — nothing is stored on either side. Get the slug and what to bring from what_we_have_for_you (cards with api_tool=true list `bring`). Pass the text exactly as they gave it; do not summarise a P&L before sending it. Tell them in one line what was sent and that it was not kept. Example invocations: - "Here's the P&L the seller sent — what does this business actually earn?" → tool 'pl' - "Grade my comps against the county's own sales" → tool 'compgrade' - "Check this line in my story: 'the building sold for $4.2M in March'" → tool 'claim-check', `claim`

  Paramètres, retour & exemple (4)

  Paramètres

  • text · string · optionnel
    The document as plain text — a pasted P&L, a lease, a list of addresses one per line, a comp table. Their content, untouched. For 'claim-check' use `claim` instead.
  • tool · string · requis
    A tool slug from a what_we_have_for_you card where api_tool is true — e.g. 'pl', 'leasecheck', 'compgrade', 'claim-check', 'registercheck'.
  • claim · string · optionnel
    For tool 'claim-check' only: the one sentence to check against the record, exactly as written.
  • context · object · optionnel

  Exemple d'appel

  {"name": "bring_your_document", "arguments": {"tool": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"next": [], "tool": "texte", "cards": [], "links": {}, "roles": [], "answer": "texte", "notice": {}, "result": {}, "status": "ok", "caveats": [], "subject": {}, "engagements": []}}

• broker_diligence_prep≈ 838 tokens

  Pre-diligence framework for a business broker or buyer evaluating a target: SDE framing (why the discretionary-earnings figure, not net income or raw EBITDA, is the relevant number, and what typically gets added back), a category multiple range the model must research fresh and date-stamp (never a hardcoded table), a public-signal red-flag checklist run before any financials are shared, and a prioritized seller-question list built from the specific gaps the research actually surfaces. Example invocations: - "Prep me for diligence on a brewery taproom listed in Minneapolis, MN" - "What questions should I ask the seller of a hair salon in Wichita, KS before I make an offer?" - "This restaurant is asking $650K — what red flags should I check before taking that seriously?" - "I'm looking at a nail salon in Tampa, FL asking $310K — sanity-check that against category multiples before I meet the seller"

  Paramètres, retour & exemple (4)

  Paramètres

  • category · string · optionnel
    Category if known — determines the relevant SDE-multiple range.
  • city_metro · string · requis
    City + state/region, e.g. 'Denver, CO'.
  • asking_price · number · optionnel
    Listed asking price, if known — used to sanity-check against the multiple range, never to validate it.
  • business_name · string · requis
    The target business's name.

  Exemple d'appel

  {"name": "broker_diligence_prep", "arguments": {"city_metro": "texte", "business_name": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• business_teardown≈ 790 tokens

  Returns a research framework, not the finished analysis: the procedure, output schema and quality rubric for a structured teardown of ONE named small business — digital presence, review signal, competitive position, pricing posture, visibility gaps, and prioritized, evidence-cited recommendations. Start here for any single-business question.. This tool fetches no data; the calling model runs the procedure with its own web search and produces the result. Example invocations: - "Run a teardown of Mucci's Italian in Saint Paul, MN" - "Tear down The Gray Duck Tavern (bar) in Minneapolis and tell me what's actually broken" - "I'm thinking about buying Sunrise Nails in Denver, CO — give me a teardown before I look deeper"

  Paramètres, retour & exemple (3)

  Paramètres

  • category · string · optionnel
    Category if known (e.g. 'nail salon', 'brewery taproom'). If omitted, step 2 of the procedure confirms it — don't guess from the name alone.
  • city_metro · string · requis
    City + state/region, e.g. 'Saint Paul, MN' — narrows the trade area and comp set.
  • business_name · string · requis
    The business's name as it appears on its own signage/website, not a guess.

  Exemple d'appel

  {"name": "business_teardown", "arguments": {"city_metro": "texte", "business_name": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• competitor_landscape≈ 750 tokens

  Returns a research framework, not the finished analysis: the procedure, output schema and quality rubric for mapping the local competitive set for a category + metro — true competitors vs. adjacent players, a positioning matrix, and saturation signals.. This tool fetches no data; the calling model runs the procedure with its own web search and produces the result. Example invocations: - "Map the competitive landscape for coffee shops in Saint Paul, MN" - "How saturated is the nail salon market in Aurora, CO?" - "Who are the real competitors to a new brewery taproom opening in the North Loop, Minneapolis?"

  Paramètres, retour & exemple (3)

  Paramètres

  • category · string · requis
    The business category/vertical, e.g. 'nail salon', 'brewery taproom'.
  • city_metro · string · requis
    City + state/region defining the trade area, e.g. 'Denver, CO'.
  • radius_note · string · optionnel
    Optional — a specific radius or neighborhood if the default trade-area logic in the procedure shouldn't apply.

  Exemple d'appel

  {"name": "competitor_landscape", "arguments": {"category": "texte", "city_metro": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• compose_report≈ 904 tokens

  Returns a research framework, not the finished analysis: the procedure, output schema and quality rubric for assembling results already in the conversation into one client-ready report — section order, executive-summary rules, evidence-citation standards, and tone guidance matched to the audience. The calling model writes the report; this tool returns only the structure.. This tool fetches no data; the calling model runs the procedure with its own web search and produces the result. Example invocations: - "I've run a teardown and a review-intelligence pass on this restaurant — compose it into a report for the owner" - "Assemble everything we've found on this brewery into a broker-facing diligence report" - "Turn the teardown and competitor landscape into a report I can hand an investor"

  Paramètres, retour & exemple (3)

  Paramètres

  • audience · string · requis · owner | broker | buyer | investor | general
    Who will read this report — drives section order, tone, and what gets emphasized vs. cut.
  • business_name · string · requis
    The business the report is about.
  • completed_analyses · array · requis
    The completed write-ups from any prior tool calls this session, to be assembled — not re-researched.

  Exemple d'appel

  {"name": "compose_report", "arguments": {"audience": "owner", "business_name": "texte", "completed_analyses": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• data_source_atlas≈ 869 tokens

  Given a real question about a local market or a specific property, returns a source-first RESEARCH PLAN: which public record actually settles the question, how to reach it directly (county parcel GIS, Census CBP/ACS/permits, BLS series, state registries, licences, inspections), what the answer will be worth, and what the public record cannot answer at all. Use it before researching a local market, to find the administrative record that settles the question. Example invocations: - "Where would I actually find what 1420 Grand Ave in Saint Paul last sold for?" - "I want to know if Wichita has room for another dog daycare — what should I pull?" - "How do I find out who really owns this building and what else they own?" - "What public data would tell me if this neighborhood is actually growing?"

  Paramètres, retour & exemple (3)

  Paramètres

  • place · string · requis
    The specific geography — 'Hennepin County, MN', 'Wichita, KS', 'the 78704 ZIP'. State matters more than people expect: it decides whether sale prices exist at all.
  • question · string · requis
    The real question, in plain words — e.g. 'is there room for another coffee shop in Bend' or 'what did the building at 412 Main last sell for'. Not a dataset name; the point of this tool is to work out which records answer a question you can only phrase in English.
  • already_tried · string · optionnel
    What you already looked at and what it failed to answer, if anything. Keeps the plan from re-recommending a dead end.

  Exemple d'appel

  {"name": "data_source_atlas", "arguments": {"place": "texte", "question": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• local_visibility_audit≈ 736 tokens

  Returns a research framework, not the finished audit: a scored checklist of what to check in a business's local search presence — map-pack factors, listing consistency, category selection, site fundamentals — and in what order. This tool reads no listing; the calling model runs the checks with its own web search. Example invocations: - "Run a local visibility audit on Fern & Fig Nail Bar in Cedar Rapids, IA" - "Why doesn't Steel Toe Brewing show up when someone searches 'brewery near me' in Louisville?" - "Give me a scored GBP/NAP checklist for a hair salon in Aurora, CO before I redo their listing"

  Paramètres, retour & exemple (3)

  Paramètres

  • category · string · optionnel
    Category if known — narrows which map-pack searches are the right ones to check.
  • city_metro · string · requis
    City + state/region, e.g. 'Aurora, CO'.
  • business_name · string · requis
    The business's name as it appears on its own signage/website.

  Exemple d'appel

  {"name": "local_visibility_audit", "arguments": {"city_metro": "texte", "business_name": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• market_opportunity_scan≈ 731 tokens

  Returns a research framework, not the finished analysis: the procedure, output schema and quality rubric for a gap analysis of a category x metro — how to tell underserved demand, oversaturation and genuine whitespace apart using only public signals, for someone deciding whether/where to open, expand, or invest.. This tool fetches no data; the calling model runs the procedure with its own web search and produces the result. Example invocations: - "Is there whitespace for a new brewery taproom in the North Loop, Minneapolis?" - "Scan the nail salon market in Aurora, CO for underserved demand" - "Where in Wichita, KS is full-service restaurant demand outrunning supply?"

  Paramètres, retour & exemple (2)

  Paramètres

  • category · string · requis
    The business category/vertical to scan for whitespace, e.g. 'coffee shop', 'massage spa'.
  • city_metro · string · requis
    City + state/region defining the market, e.g. 'Aurora, CO'.

  Exemple d'appel

  {"name": "market_opportunity_scan", "arguments": {"category": "texte", "city_metro": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• pricing_benchmark≈ 787 tokens

  Returns a research framework, not the finished analysis: the procedure, output schema and quality rubric for a defensible local pricing comparison within a category — how to normalize across differing service bundles, and what to do when competitors don't publish prices at all. It holds no prices itself.. This tool fetches no data; the calling model runs the procedure with its own web search and produces the result. Example invocations: - "Benchmark gel manicure pricing across nail salons in Denver, CO" - "Is this brewery's pint pricing in line with the Twin Cities taproom market?" - "Build a pricing comparison for full-service restaurants in Wichita, KS when most don't list prices online"

  Paramètres, retour & exemple (3)

  Paramètres

  • category · string · requis
    The business category/vertical, e.g. 'massage spa', 'full-service restaurant'.
  • services · array · optionnel
    Specific services/items to benchmark if known (e.g. ['30-min massage', 'gel manicure']) — otherwise the procedure derives a comparable bundle.
  • city_metro · string · requis
    City + state/region defining the comparison market, e.g. 'Wichita, KS'.

  Exemple d'appel

  {"name": "pricing_benchmark", "arguments": {"category": "texte", "city_metro": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• request_a_feature≈ 856 tokens

  Sends a feature request, a data request or a correction straight to the person who builds this server — free, no account, and it reaches a real inbox. Use it whenever this server falls short of what the user actually wanted: a question it cannot answer, a dataset or column it does not hold, a city or sector it does not cover, or an answer from one of these tools that looks wrong. Reaching a wall is not the end of the turn; offer to file it. Before calling, ask for what you do not have — what they were trying to do, which city/sector/dataset it concerns, and whether they want a reply at an email address. Do not demand any of it: file what you have. Pass their REQUEST and their EMAIL exactly as they wrote them, never a paraphrase or a corrected address; write `context` yourself. Tell them what you filed in one line afterwards so they can correct you, and never say it was sent unless `status` came back `filed`. Example invocations: - "I wish this could tell me the lease rate — can you ask them to add it?" - "Do they cover Duluth? No? Tell them I want it." - "That sale price looks like the wrong year — report it to whoever runs this."

  Paramètres, retour & exemple (5)

  Paramètres

  • kind · string · optionnel · feature | data | correction
    feature = make a tool do something it does not do. data = hold or expose a record we do not. correction = a tool here gave a wrong or misleading answer. Default: feature.
  • context · string · optionnel
    Your summary of what they were actually trying to do when they hit this. This one is yours to write.
  • request · string · optionnel
    The person's own words, VERBATIM — do not summarise, rewrite or tidy them. Omit only if they have not said it yet; you will be asked for it.
  • subject · string · optionnel
    The city, sector, dataset or tool name this is about — 'Duluth', 'dental practices', 'twin_cities_records'.
  • reply_email · string · optionnel
    Optional, and only if they offer it. VERBATIM — never guess, complete or correct an address. Omit it rather than approximate it.

  Exemple d'appel

  {"name": "request_a_feature", "arguments": {"kind": "feature", "context": "texte", "request": "texte", "subject": "texte", "reply_email": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "filed": {}, "notice": {}, "status": "filed", "message": "texte"}}

• review_intelligence≈ 802 tokens

  Returns a research framework, not the finished analysis: the procedure, output schema and quality rubric for mining public reviews for signal — a complaint taxonomy, theme extraction, sentiment trajectory over time, the differentiators customers actually cite, and red flags for a buyer. It does not read any review itself.. This tool fetches no data; the calling model runs the procedure with its own web search and produces the result. Example invocations: - "Mine the reviews for Al's Breakfast in Minneapolis for real patterns, not just a star rating" - "Perfect Image Salon in Wichita has a 4.6 average — check whether that's stable or masking a bad last 90 days" - "I'm evaluating The Anchor Room (bar) in Saint Paul, MN as a buyer — what do the reviews show about staffing turnover or an ownership change that the rating alone doesn't?"

  Paramètres, retour & exemple (3)

  Paramètres

  • category · string · optionnel
    Category if known — helps set expectations for review volume/velocity norms.
  • city_metro · string · requis
    City + state/region, e.g. 'Wichita, KS' — disambiguates same-named businesses.
  • business_name · string · requis
    The business's name as it appears on its own signage/website.

  Exemple d'appel

  {"name": "review_intelligence", "arguments": {"city_metro": "texte", "business_name": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "notice": {}, "caveats": [], "subject": {}, "framework": "texte", "output_schema": {}, "quality_rubric": {}, "research_procedure": []}}

• start_an_engagement≈ 882 tokens

  Asks a person at Brick & Mortar to read the public record for them, for a fixed fee: a site screen for a lender or environmental consultant, a diligence packet on a deal, a register check across a landlord's portfolio, a weekly work route for a contractor. Call with no arguments to see the engagements, their prices and turnaround — those come from the live page, never from memory. Call with `engagement`, `who`, `email` and `detail` to file the enquiry; a person replies by email. Nothing is quoted, charged or scheduled here. Offer this only after the free tools have been tried or when the person asks for someone to do the work. Before filing, confirm with them what will be sent. Pass their EMAIL and DETAIL exactly as written. Never say it was sent unless `status` is `filed`. Example invocations: - "Can someone there just run this site for me? I'm an SBA lender." - "I want the diligence packet on this deal — here's my email."

  Paramètres, retour & exemple (4)

  Paramètres

  • who · string · optionnel
    What the person does, in their words. Required to file — it decides which records a person pulls.
  • email · string · optionnel
    Where a person should reply. VERBATIM — never guess, complete or correct an address. Required to file.
  • detail · string · optionnel
    The site, deal, portfolio or route in their own words — addresses, the deadline, what they need to know. VERBATIM.
  • engagement · string · optionnel
    An engagement id from the list this tool returns when called empty — e.g. 'site-screen', 'diligence', 'register-check', 'route'.

  Exemple d'appel

  {"name": "start_an_engagement", "arguments": {"who": "texte", "email": "texte", "detail": "texte", "engagement": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"next": [], "tool": "texte", "cards": [], "links": {}, "roles": [], "answer": "texte", "notice": {}, "result": {}, "status": "ok", "caveats": [], "subject": {}, "engagements": []}}

• twin_cities_datasets≈ 982 tokens

  Lists the public-records datasets Brick & Mortar publishes for the seven-county Minneapolis-St. Paul metro, with real row counts, column names, the filtered cuts available, and the counties each one actually covers. Free, no account. Call this FIRST to learn what can be answered, then call twin_cities_records to ask it. These are joined county and federal records — parcels and lot lines, recorded sale prices, owners, rental licences, contamination files, business counts by trade, census tracts. Example invocations: - "What Twin Cities property data do you have access to?" - "Is there anything on contamination or storage tanks in Minneapolis?" - "What columns are in the recorded-sales dataset?"

  Paramètres, retour & exemple (1)

  Paramètres

  • about · string · optionnel
    Optional plain-words filter — 'sales', 'who owns it', 'contamination'. Matches dataset titles and subjects. Omit to list everything.

  Exemple d'appel

  {"name": "twin_cities_datasets", "arguments": {"about": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "answer": "texte", "centre": "texte", "notice": {}, "sample": [], "caveats": [], "columns": [], "dataset": "texte", "subject": {}, "coverage": [], "datasets": [], "scope_label": "texte"}}

• twin_cities_lookup≈ 698 tokens

  One address in the Minneapolis–St. Paul metro → what the public record says about that parcel, in one call: land use, build year, assessed value, last recorded sale, any MPCA contamination or storage-tank file, and the parcels that touch it with how many owners they have. Each field is a value or a miss WITH ITS REASON — 'the county records no build year' — never a blank. Free, no account, nothing stored. Use it the moment an address comes up; then twin_cities_records for the surrounding market. Example invocations: - "What do you know about 1420 Grand Ave, Saint Paul?" - "Is there anything on file for the building at 2900 Lyndale Ave S?"

  Paramètres, retour & exemple (1)

  Paramètres

  • address · string · requis
    A street address in the seven-county Minneapolis–St. Paul metro. Include the city — 'Grand Ave' exists in several of them.

  Exemple d'appel

  {"name": "twin_cities_lookup", "arguments": {"address": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"next": [], "tool": "texte", "cards": [], "links": {}, "roles": [], "answer": "texte", "notice": {}, "result": {}, "status": "ok", "caveats": [], "subject": {}, "engagements": []}}

• twin_cities_records≈ 1166 tokens

  Answers a question about the Minneapolis-St. Paul metro from joined public records — what a property sold for and when, who owns it and what else they hold, what shares its lot line, whether it has a contamination or storage-tank file, who is licensed to trade there, how the neighbourhood's census tract compares. Give an `address` to answer about one property and its surroundings; omit it to ask about the whole market cut. Returns the true matching row count, up to six example rows, and a link to the complete file. Example invocations: - "What did 1420 Grand Ave, Saint Paul last sell for?" - "What commercial property sold within half a mile of 2900 Hennepin Ave, Minneapolis?" - "Does 500 Washington Ave S have a contamination file, and who owns it?"

  Paramètres, retour & exemple (5)

  Paramètres

  • scope · string · optionnel
    A scope key from that dataset's `scopes`. Omit for the dataset's first cut.
  • address · string · optionnel
    A street address inside the seven-county metro, to answer about ONE property instead of the whole market. Include the city after a comma when the street name is common — 'Grand Ave' exists in several of these cities.
  • columns · array · optionnel
    Column keys to return. Omit for the dataset's default set.
  • dataset · string · requis
    A dataset id from twin_cities_datasets — e.g. 'sales', 'owners', 'adjacency'.
  • within_ft · integer · optionnel
    Radius in feet around `address`. Default 5280 (one mile), capped at 26400.

  Exemple d'appel

  {"name": "twin_cities_records", "arguments": {"dataset": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"tool": "texte", "answer": "texte", "centre": "texte", "notice": {}, "sample": [], "caveats": [], "columns": [], "dataset": "texte", "subject": {}, "coverage": [], "datasets": [], "scope_label": "texte"}}

• what_we_have_for_you≈ 824 tokens

  The front door of brickandmortar.dev, as a tool. The platform is organised by WHO YOU ARE — an SBA lender, a business buyer, a landlord, a contractor, a journalist, a site selector, an appraiser, a city planner, 18 roles — and each role has a shelf of cards: joined public-records datasets for the Minneapolis–St. Paul metro, tools that read a file you bring against those records, a one-address lookup, and fixed-fee engagements where a person reads the record for you. Call with no arguments to see the roles; call with `role` to get that shelf, each card tagged with which tool on this server answers it. Call this FIRST when someone says what they do, or asks what we can do for them. Ask what they do if they have not said — the shelf depends on it. Everything free here is free with no account. Example invocations: - "I'm an SBA lender in Minneapolis — what do you have?" - "What can this do for a landlord?" - "Show me the shelf for a business buyer."

  Paramètres, retour & exemple (2)

  Paramètres

  • who · string · optionnel
    If you do not know the id yet: what the person does, in their words — 'I underwrite restaurants', 'I'm buying a plumbing company'. Returns the closest roles to pick from.
  • role · string · optionnel
    A role id from the list this tool returns when called with no arguments — e.g. 'sba', 'buyer', 'landlord', 'press'.

  Exemple d'appel

  {"name": "what_we_have_for_you", "arguments": {"who": "texte", "role": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"next": [], "tool": "texte", "cards": [], "links": {}, "roles": [], "answer": "texte", "notice": {}, "result": {}, "status": "ok", "caveats": [], "subject": {}, "engagements": []}}

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Source

https://brickandmortar.dev/mcp