Yatmo MCP server
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Yatmo MCP server What's the nearest school and supermarket to 50.8467, 4.3525 in Belgium?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Yatmo MCP server: neighbourhood data for AI assistants and agents
Yatmo exposes its neighbourhood intelligence to AI clients through a remote MCP server. Connect Claude, Cursor, VS Code or your own agent, and it can answer "what is around this property?" with real data: nearest school, nursery, supermarket, public transport, train station, motorway access, distances with travel times, and a quotable summary paragraph, in 25 countries.
Two ways to use it: the hosted server operated by Yatmo at https://mcp.yatmo.com/mcp/v1 (Streamable HTTP, nothing to
install), or the local edition in this repository (npx -y @yatmo/mcp, stdio), which exposes the same four tools and
calls the Yatmo API with your key. This repository also holds ready-to-copy client configurations and a quick start. Full documentation:
documentation.yatmo.com/mcp.
The four tools
Tool | Returns |
| A human-readable neighbourhood paragraph plus the nearest school, nursery, supermarket, transport and the reverse-geocoded place |
| An ordered list of points of interest around a property, optionally filtered by category, within a radius |
| The single nearest place of a category, with distance and travel time |
| Public-transport presence, nearest stop, nearest train station, nearest motorway access |
All tools take latitude, longitude, country (BE, FR, UK...) and language, and return the same envelope:
text (a summary the assistant can quote), facts (structured data), warnings and metadata. Categories use one
vocabulary everywhere: school, nursery, supermarket, public_transport, train_station, motorway.
Details: tools reference.
Related MCP server: real-estate-data-mcp-server
Authentication
Every request carries your Yatmo backend key in the LicenceKey header (LicenseKey is accepted too). Keep the
key in the client's secret store, never in a public web page: the key grants access to your Yatmo account. Get a key at
yatmo.com; keys explained.
Connect a client
Generic remote-MCP configuration (configs/mcp.json), accepted by most clients, sometimes under a slightly different parent key:
{
"mcpServers": {
"yatmo": {
"url": "https://mcp.yatmo.com/mcp/v1",
"headers": { "LicenceKey": "YOUR_KEY" }
}
}
}Claude.ai and Claude Desktop: Settings > Connectors > Add custom connector, URL https://mcp.yatmo.com/mcp/v1,
custom header LicenceKey: YOUR_KEY. The four tools appear once the connector is enabled.
Claude Code:
claude mcp add --transport http yatmo https://mcp.yatmo.com/mcp/v1 --header "LicenceKey: YOUR_KEY"Cursor: copy configs/cursor/mcp.json to .cursor/mcp.json in your project (or to
~/.cursor/mcp.json for every project).
VS Code (Copilot agent mode): copy configs/vscode/mcp.json to .vscode/mcp.json.
Your own agent (Anthropic, OpenAI or any LLM SDK): use an MCP client library (TypeScript, Python, .NET, Go) with the
Streamable HTTP transport, add the LicenceKey header to the transport, and forward the four tool definitions to the
model. quickstart.py shows the raw protocol without any library.
Run it locally (stdio)
For clients that prefer a local process, or to inspect the code: @yatmo/mcp on npm, Node.js 18 or later, two
dependencies (the official MCP SDK and zod). Same tools, same JSON answers as the hosted server; the key is read from the
LicenceKey environment variable and sent to the Yatmo API only.
{
"mcpServers": {
"yatmo": {
"command": "npx",
"args": ["-y", "@yatmo/mcp"],
"env": { "LicenceKey": "YOUR_YATMO_KEY" }
}
}
}From a clone: npm install, then LicenceKey=YOUR_YATMO_KEY node src/index.js. Tests: npm test (mapping checked on a
real API answer). Listing the tools works without a key; a tool call without one returns a 401 envelope.
Try it from the command line
export YATMO_KEY=your_backend_key
python3 quickstart.py # initialize, list the tools, call yatmo_get_location_summary on a Brussels address
sh quickstart.sh # the same three calls with curlPrompts that work well
"What is the neighbourhood like around 50.8467, 4.3525 in Belgium? Answer in French."
"Nearest primary school and supermarket to this listing, with travel times on foot."
"Is this address well served by public transport? Nearest train station and motorway access?"
"List the points of interest within 800 m of the property, schools and supermarkets only."
The assistant answers from the text and facts of the tools; distances come with the mode they were measured in.
More
The same data for websites: plugins, WordPress, Odoo, mobile SDKs, examples
MIT licence for the files of this repository. Yatmo is a paid service for real estate portals, agency networks and developers.
Directories
The server is published in the official MCP registry as com.yatmo/yatmo (server.json in this repository) and submitted to the Docker MCP Catalog, Smithery and mcp.so. In clients that read the registry, search for "yatmo" or "real estate".
Available Tools
4 toolsyatmo_get_accessibility_profileAccessibility profile of a propertyARead-onlyIdempotentInspect
Returns a synthetic accessibility view of a property: public-transport presence and nearest stop distance. Use it to answer questions like 'is this property practical without a car?'.
Inputs: country (required), language (required), latitude (required), longitude (required).
yatmo_get_location_summaryNeighbourhood summary of a propertyARead-onlyIdempotentInspect
Returns a human-readable neighbourhood summary plus structured facts (nearest school, nearest supermarket, public transport) around a property location. Use this to explain why a property is well situated.
Inputs: country (required), language (required), latitude (required), longitude (required).
yatmo_get_nearby_poisPoints of interest around a propertyBRead-onlyIdempotentInspect
Lists points of interest around a property, optionally filtered by category, within a radius. Categories: school, nursery, supermarket, public_transport, train_station, motorway. 'school' returns actual schools (excluding daycare); 'nursery' returns crèches/daycare only.
Inputs: country (required), language (required), latitude (required), longitude (required), +3 more.
yatmo_get_nearest_by_categoryNearest place of a categoryARead-onlyIdempotentInspect
Returns the single nearest point of interest of a given category (e.g. the nearest school) with distance and travel time. Categories: school, nursery, supermarket, public_transport, train_station, motorway.
Inputs: country (required), category (required), language (required), latitude (required), +1 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
The four tools overlap substantially: yatmo_get_location_summary already reports nearest school, supermarket, and public transport, which duplicates what yatmo_get_nearest_by_category returns, and yatmo_get_accessibility_profile re-covers public-transport/nearest-stop data. Descriptions do clarify the granularity (aggregated summary vs. list vs. single nearest vs. derived accessibility view), so an agent can mostly choose correctly, but the boundaries are fuzzy.
All tools follow a strict 'yatmo_get_<noun>' snake_case pattern (get_location_summary, get_nearby_pois, get_nearest_by_category, get_accessibility_profile). The verb and namespace prefix are uniform throughout with no deviation.
Four tools is a tight but sensible scope for a neighbourhood/property-location service, and each tool maps to a distinct query granularity. It is on the lean side, but no tool feels redundant enough to be dead weight.
The surface covers the core location workflows: aggregate summary, filtered POI listing, single nearest POI with distance/travel time, and an accessibility view. Minor gaps exist (e.g., no batch/multi-category query or distance/commute matrix), but agents can compose the existing tools to cover most questions.
Maintenance
Related MCP Connectors
UK area & property intelligence for AI agents: reports, EPC, comparables, with source provenance.
Sourced facts, explained scores and change alerts for every GB neighbourhood, from one API.
- Casafari MCPDeprecatedcom.casafari
Property data intelligence, built for AI agents: Europe's most complete property index.
UK property research tools - crime stats, schools, demographics, valuations for AI.
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