Small Business Intelligence MCP server
{
"mcpServers": {
"2016judea-small-business-intelligence": {
"type": "http",
"url": "https://brickandmortar.dev/mcp"
}
}
}{
"mcpServers": {
"2016judea-small-business-intelligence": {
"type": "http",
"url": "https://brickandmortar.dev/mcp"
}
}
}{
"mcpServers": {
"2016judea-small-business-intelligence": {
"type": "http",
"url": "https://brickandmortar.dev/mcp"
}
}
}{
"servers": {
"2016judea-small-business-intelligence": {
"type": "http",
"url": "https://brickandmortar.dev/mcp"
}
}
}{
"mcpServers": {
"2016judea-small-business-intelligence": {
"type": "http",
"url": "https://brickandmortar.dev/mcp"
}
}
}file .mcp.json (in your project root)file claude_desktop_config.jsonfile ~/.cursor/mcp.jsonfile .vscode/mcp.jsonfile ~/.codeium/windsurf/mcp_config.jsonversion 0.2.0
What it does
An MCP server that teaches AI models how to research small businesses and locate public records. It provides nine methodology tools that guide research procedures, plus two tools that query joined public records for the Minneapolis-St. Paul metro area covering parcels, sales, licenses, and business data.
- Research a small business by following a structured teardown that examines presence, reviews, competitive position, and pricing.
- Query Twin Cities public records to find parcel data, recorded sale prices, owners, rental licenses, and contamination files for a specific address.
- Develop a data source research plan that identifies which public records answer a question, how to access them, and what they cannot reveal.
Tools this server exposes
| Tool | Does |
|---|---|
| data_source_atlas | Given a question and location, returns a research plan showing which public record settles it, how to reach it, and what it cannot answer. |
| business_teardown | Provides a structured analysis of a named business including presence, review signals, competitive position, and prioritized recommendations. |
| competitor_landscape | Maps the local competitive set by identifying true competitors, positioning, and saturation signals against administrative establishment counts. |
| review_intelligence | Analyzes public reviews to extract complaint themes, sentiment trends, and red flags. |
| local_visibility_audit | Audits local search presence including map-pack factors, listing consistency, and site fundamentals with a scored checklist. |
| pricing_benchmark | Develops a defensible local pricing comparison with guidance on normalizing across different bundles. |
| broker_diligence_prep | Prepares pre-diligence materials for brokers and buyers including SDE framing, multiple ranges, red flags, and county property records. |
| market_opportunity_scan | Analyzes a category and metro for underserved demand and identifies gaps that may indicate market challenges. |
| compose_report | Assembles prior tool outputs into a client-ready report matched to the intended audience. |
| twin_cities_datasets | Describes available joined public records for the seven-county Minneapolis-St. Paul metro including row counts, columns, and geographic coverage. |
| twin_cities_records | Queries Twin Cities public records for a specific property or market segment, returning row count, example rows, and a link to complete results. |
| request_a_feature | Files a feature request, data request, or correction to the server maintainer. |
Before you enable it
An MCP server runs with whatever access you give it. Start with a development account rather than production, and check which tools the server exposes before you let an agent call them unattended.