Small Business Intelligence by Brick & Mortar
Free joined public records for small business and CRE: Twin Cities parcels, sales, licences
| Endpoint | https://brickandmortar.dev/mcp (streamable-http) |
|---|---|
| Registry version | 0.2.0 |
| Now | 16 tools · grade C (75) · pin ts_13e68355d424b80c567a55c83ff007da · protocol 2025-06-18 |
| First / last listed | 2026-10-25 01:45 UTC / 2026-10-25 01:45 UTC |
| Grade over time | 2026-10-25: C (75), 16 tools |
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Tools: data_source_atlas what_we_have_for_you twin_cities_lookup twin_cities_datasets twin_cities_records bring_your_document business_teardown competitor_landscape review_intelligence local_visibility_audit pricing_benchmark broker_diligence_prep market_opportunity_scan compose_report start_an_engagement request_a_feature
data_source_atlas | 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, params: question*, place*, already_tried |
what_we_have_for_you | 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 look params: role, who |
twin_cities_lookup | 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 params: address* |
twin_cities_datasets | 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 params: about |
twin_cities_records | 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 a params: dataset*, scope, address, within_ft, columns |
bring_your_document | 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 params: tool*, text, claim, context |
business_teardown | 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 p params: business_name*, city_metro*, category |
competitor_landscape | 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 params: category*, city_metro*, radius_note |
review_intelligence | 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 ow params: business_name*, city_metro*, category |
local_visibility_audit | 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 params: business_name*, city_metro*, category |
pricing_benchmark | 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 p params: category*, city_metro*, services |
broker_diligence_prep | 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 prio params: business_name*, city_metro*, category, asking_price |
market_opportunity_scan | 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 sear params: category*, city_metro* |
compose_report | 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 params: business_name*, audience*, completed_analyses* |
start_an_engagement | 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 `engag params: engagement, who, email, detail |
request_a_feature | 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 i params: request, kind, context, subject, reply_email |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.