# Small Business Intelligence by Brick & Mortar (remote · brickandmortar.dev)

Free joined public records for small business and CRE: Twin Cities parcels, sales, licences

- Trust score: 82/100 (high trust)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-21

## Components

- remote · `brickandmortar.dev`: 82/100 (this document), [markdown](https://verifymcp.io/servers/2016judea-small-business-intelligence/brickandmortar.md), [page](https://verifymcp.io/servers/2016judea-small-business-intelligence/brickandmortar)

## Channel facts

- Endpoint: `https://brickandmortar.dev/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.2.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-21.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 67/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2922 tokens (~243/item across 12 items; 12 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 73/100
  - Stability observed for 22 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 12 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 12 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### How do I install the Small Business Intelligence by Brick & Mortar MCP server?

Small Business Intelligence by Brick & Mortar is a hosted endpoint at https://brickandmortar.dev/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http 2016judea-small-business-intelligence 'https://brickandmortar.dev/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "2016judea-small-business-intelligence": {
      "url": "https://brickandmortar.dev/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "2016judea-small-business-intelligence": {
      "type": "http",
      "url": "https://brickandmortar.dev/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.2016judea-small-business-intelligence]
url = "https://brickandmortar.dev/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "2016judea-small-business-intelligence": {
      "type": "remote",
      "url": "https://brickandmortar.dev/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add 2016judea-small-business-intelligence --url 'https://brickandmortar.dev/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  2016judea-small-business-intelligence:
    url: "https://brickandmortar.dev/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "2016judea-small-business-intelligence": {
      "Transport": "http",
      "Url": "https://brickandmortar.dev/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add 2016judea-small-business-intelligence -t streamable-http -u 'https://brickandmortar.dev/mcp'
```

### Other

```json
{
  "mcpServers": {
    "2016judea-small-business-intelligence": {
      "type": "http",
      "url": "https://brickandmortar.dev/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-20 (score 82, +1)

No change was recorded against any check on this day. Stability & Change Management went from 67 to 70. That category is still filling its 30-day observation window: 20 days of observed history at the previous scan, 21 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-18 (score 81, +1)

No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-16 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-13 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-11 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-09 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-07 (score 76, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-05 (score 75, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (12)

### `data_source_atlas` (~342 tokens)

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 this BEFORE researching a local market — it is the difference between reading whatever a search engine surfaced and pulling the administrative record that settles it.

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?"

Input parameters:

- `already_tried` (string): What you already looked at and what it failed to answer, if anything. Keeps the plan from re-recommending a dead end.
- `place` (string, required): 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, required): 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 ou…

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `twin_cities_datasets` (~190 tokens)

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 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?"

Input parameters:

- `about` (string): Optional plain-words filter — 'sales', 'who owns it', 'contamination'. Matches dataset titles and subjects. Omit to list everything.

Output parameters:

- `answer` (string)
- `caveats` (array)
- `centre`
- `columns` (array)
- `coverage` (array): The counties this dataset actually holds. Coverage is not uniform across datasets.
- `dataset` (string)
- `datasets` (array)
- `documented_at` (string): Page documenting this dataset's source, full column list and stated limits.
- `download_url` (string): Fetch this for the complete file.
- `matching_rows` (integer): The true number of rows that match. `sample` shows at most six of them.
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, no other field carries a result.
- `sample` (array): At most six example rows. Never report these as the complete result.
- `scope_label` (string)
- `subject` (object)
- `tool` (string)

### `twin_cities_records` (~332 tokens)

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 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?"

Input parameters:

- `address` (string): 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…
- `columns` (array): Column keys to return. Omit for the dataset's default set.
- `dataset` (string, required): A dataset id from twin_cities_datasets — e.g. 'sales', 'owners', 'adjacency'.
- `scope` (string): A scope key from that dataset's `scopes`. Omit for the dataset's first cut.
- `within_ft` (integer): Radius in feet around `address`. Default 5280 (one mile), capped at 26400.

Output parameters:

- `answer` (string)
- `caveats` (array)
- `centre`
- `columns` (array)
- `coverage` (array): The counties this dataset actually holds. Coverage is not uniform across datasets.
- `dataset` (string)
- `datasets` (array)
- `documented_at` (string): Page documenting this dataset's source, full column list and stated limits.
- `download_url` (string): Fetch this for the complete file.
- `matching_rows` (integer): The true number of rows that match. `sample` shows at most six of them.
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, no other field carries a result.
- `sample` (array): At most six example rows. Never report these as the complete result.
- `scope_label` (string)
- `subject` (object)
- `tool` (string)

### `business_teardown` (~217 tokens)

Business Teardown

Full structured teardown of ONE named small business: digital presence, review signal, competitive position, pricing posture, visibility gaps, and prioritized, evidence-cited recommendations. The flagship tool — start here for any single-business question.

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"

Input parameters:

- `business_name` (string, required): The business's name as it appears on its own signage/website, not a guess.
- `category` (string): 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, required): City + state/region, e.g. 'Saint Paul, MN' — narrows the trade area and comp set.

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `competitor_landscape` (~167 tokens)

Competitor Landscape

Maps the local competitive set for a category + metro: true competitors vs. adjacent players, a positioning matrix, and saturation signals.

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?"

Input parameters:

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

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `review_intelligence` (~204 tokens)

Review Intelligence

Mines public reviews for signal: a complaint taxonomy, theme extraction, sentiment trajectory over time, the differentiators customers actually cite, and red flags for a buyer.

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?"

Input parameters:

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

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `local_visibility_audit` (~182 tokens)

Local Visibility Audit

Audits a business's local search presence: map-pack factors, listing consistency, category selection, site fundamentals — what to check, and in what order — returned as a scored checklist.

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"

Input parameters:

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

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `pricing_benchmark` (~183 tokens)

Pricing Benchmark

Builds 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.

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"

Input parameters:

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

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `broker_diligence_prep` (~286 tokens)

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 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"

Input parameters:

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

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `market_opportunity_scan` (~153 tokens)

Market Opportunity Scan

Gap analysis for a category x metro: detects underserved demand, oversaturation, and genuine whitespace using only public signals — for someone deciding whether/where to open, expand, or invest.

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?"

Input parameters:

- `category` (string, required): The business category/vertical to scan for whitespace, e.g. 'coffee shop', 'massage spa'.
- `city_metro` (string, required): City + state/region defining the market, e.g. 'Aurora, CO'.

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `compose_report` (~203 tokens)

Compose Report

Assembles the outputs of any prior Small Business Intelligence tool calls into one polished, client-ready report: section order, executive-summary rules, evidence-citation standards, and tone guidance matched to the audience. This is what makes a multi-tool session feel like a finished product, not a pile of separate answers.

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"

Input parameters:

- `audience` (string, required): Who will read this report — drives section order, tone, and what gets emphasized vs. cut.
- `business_name` (string, required): The business the report is about.
- `completed_analyses` (array, required): The completed write-ups from any prior tool calls this session, to be assembled — not re-researched.

Output parameters:

- `caveats` (array)
- `framework` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
- `output_schema` (object)
- `quality_rubric` (object)
- `research_procedure` (array)
- `subject` (object)
- `tool` (string)

### `request_a_feature` (~463 tokens)

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 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."

Input parameters:

- `context` (string): Your summary of what they were actually trying to do when they hit this. This one is yours to write.
- `kind` (string): 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.
- `reply_email` (string): Optional, and only if they offer it. VERBATIM — never guess, complete or correct an address. Omit it rather than approximate it.
- `request` (string): 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): The city, sector, dataset or tool name this is about — 'Duluth', 'dental practices', 'twin_cities_records'.

Output parameters:

- `filed` (object)
- `message` (string)
- `notice` (object): Present ONLY when the request was denied by usage policy instead of executed. When present, nothing was filed.
- `status` (string): `needs_more` means nothing was sent and you should ask the person the question in `message`, then call again. `not_filed` means it failed — do NOT tell them it was submitted.
- `tool` (string)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/2016judea-small-business-intelligence/brickandmortar#diagnostics

## Score history

- 2026-09-21: 82
- 2026-09-20: 82
- 2026-09-19: 81
- 2026-09-18: 81
- 2026-09-17: 80
- 2026-09-16: 80
- 2026-09-15: 79
- 2026-09-14: 79
- 2026-09-13: 79
- 2026-09-12: 78
- 2026-09-11: 78
- 2026-09-10: 77
- 2026-09-09: 77
- 2026-09-08: 76
- 2026-09-07: 76
- 2026-09-06: 75
- 2026-09-05: 75
- 2026-09-04: 74
- 2026-09-03: 74
- 2026-09-02: 73
- 2026-09-01: 73
- 2026-08-31: 73
- 2026-08-30: 72

## Common questions

### What is the Small Business Intelligence by Brick & Mortar MCP server?

Small Business Intelligence by Brick & Mortar is an MCP server listed in the public MCP registry as io.github.2016judea/small-business-intelligence. Free joined public records for small business and CRE: Twin Cities parcels, sales, licences. This page covers its hosted endpoint (https://brickandmortar.dev/mcp).

### Is the Small Business Intelligence by Brick & Mortar MCP server safe to use?

Small Business Intelligence by Brick & Mortar scores 82 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the Small Business Intelligence by Brick & Mortar MCP server expose?

Small Business Intelligence by Brick & Mortar exposes 12 tools: data_source_atlas, twin_cities_datasets, twin_cities_records, business_teardown, competitor_landscape, and 7 more. Their descriptions and schemas cost roughly 2,922 tokens of context every time the server is loaded.

### Does the Small Business Intelligence by Brick & Mortar MCP server require authentication?

No. We connected to Small Business Intelligence by Brick & Mortar without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the Small Business Intelligence by Brick & Mortar MCP server still maintained?

Small Business Intelligence by Brick & Mortar is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://brickandmortar.dev/mcp
- Repository: https://github.com/2016judea/small-business-intelligence-mcp
- Website: https://brickandmortar.dev/connect/
- Changelog RSS feed: https://verifymcp.io/servers/2016judea-small-business-intelligence/brickandmortar.xml
- Changelog JSON feed: https://verifymcp.io/servers/2016judea-small-business-intelligence/brickandmortar.json
- HTML version of this page: https://verifymcp.io/servers/2016judea-small-business-intelligence/brickandmortar