OSM MCP Analytics Server
Provides geospatial analytics and tools including geocoding, routing, nearby place search, neighborhood livability scoring, and commute analysis using OpenStreetMap data.
Click 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., "@OSM MCP Analytics Serveranalyze neighborhood livability in downtown Seattle"
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.
Enterprise OpenStreetMap (OSM) MCP Analytics Server
A modular, high-performance Model Context Protocol (MCP) server for OpenStreetMap. This implementation goes beyond simple geocoding, providing advanced geospatial analytics like neighborhood livability scoring and commute analysis.
š Why this server?
While there are many OSM MCP servers, this version is built for stability, modular extension, and advanced insights:
Modular Architecture: Clean separation between API Client, Utility logic, and Tool definitions. Easy to audit and extend.
Enterprise Analytics:
analyze_neighborhood: Calculates a "Livability Score" based on walking proximity to essential services (groceries, healthcare, parks).analyze_commute: Multi-modal comparison of travel times (car, bike, foot) for lifestyle planning.
Efficient: Uses FastMCP for asynchronous I/O and structured tool registration.
Windows Optimized: Built and tested to run reliably as a background process on Windows environments.
Related MCP server: chuk-mcp-geocoder
š Features
Core Tools
Geocoding:
geocode_address,reverse_geocodeRouting:
get_route_directions(OSRM based)Search:
find_nearby_places,search_categoryAnalytics:
explore_area,analyze_neighborhood,analyze_commute
Specialized Tools (Optional)
Found in tools/extras.py (not loaded by default for performance):
find_schools_nearbyfind_ev_charging_stationsfind_parking_facilitiessuggest_meeting_point
Resources
location://place/{query}: Real-time place metadata.location://map/{style}/{z}/{x}/{y}: Interactive map tile retrieval.
š¦ Installation
Requirements
Python 3.10+
uv(recommended) orpip
Method 1: Via MCP Config (Claude/Cursor)
Add this to your mcp_config.json:
{
"mcpServers": {
"osm-mcp": {
"command": "python",
"args": [
"c:/path/to/osm-mcp-server/src/openstreetmap_mcp/server.py"
],
"env": {
"PYTHONPATH": "c:/path/to/osm-mcp-server/src/openstreetmap_mcp"
}
}
}
}Method 2: Local Development
git clone https://github.com/neco001/openstreetmap-mcp
cd openstreetmap-mcp
uv syncš Project Structure
src/openstreetmap_mcp/
āāā server.py # Main Entry Point
āāā instance.py # FastMCP lifecycle
āāā client.py # HTTP logic for OSM/OSRM/Overpass
āāā utils.py # Haversine & geometric helpers
āāā tools/ # Categorized tool definitions
ā āāā geocoding.py
ā āāā routing.py
ā āāā search.py
ā āāā analysis.py
āāā resources.py # Map & Data resourcesāļø License
MIT License - feel free to use, modify and distribute.
Acknowledgments
Original logic & concepts by Jagan Shanmugam. This repository is a modular refactor focused on Enterprise usage, Windows compatibility, and Analytics tools.
This server cannot be deployed
Maintenance
Related MCP Connectors
OpenStreetMap queries, maps/styles, search, routing, terrain, analysis, pipelines, and rendering.
Geocode, reverse geocode, and run Overpass spatial queries on OpenStreetMap data.
Geocode, reverse geocode, and run Overpass spatial queries on OpenStreetMap data.
- HoodExOAuthapp.hoodex
Neighbourhood intelligence for any address: livability scores, community and street data.
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