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Openstreetmap

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Advanced OpenStreetMap MCP Server for AI Agents. Goes beyond geocoding with Neighborhood Livability Scoring and Commute Analysis. Modular, stable, and Windows-r

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Advanced OpenStreetMap MCP Server for AI Agents. Goes beyond geocoding with Neighborhood Livability Scoring and Commute Analysis. Modular, stable, and Windows-ready.

README

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.

🚀 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.

🛠 Features

Core Tools

  • Geocoding: geocode_address, reverse_geocode
  • Routing: get_route_directions (OSRM based)
  • Search: find_nearby_places, search_category
  • Analytics: explore_area, analyze_neighborhood, analyze_commute

Specialized Tools (Optional)

Found in tools/extras.py (not loaded by default for performance):

  • find_schools_nearby
  • find_ev_charging_stations
  • find_parking_facilities
  • suggest_meeting_point

Resources

  • location://place/{query}: Real-time place metadata.
  • location://map/{style}/{z}/{x}/{y}: Interactive map tile retrieval.

📦 Installation

Requirements

  • Python 3.10+
  • uv (recommended) or pip

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 sync

📂 Project Structure

src/openstreetmap_mcp/
├── server.py           # Main Entry Point
├── instance.py         # FastMCP lifecycle
├── client.py           # HTTP logic for OSM/OSRM/Overpass
├── utils.py            # Haversine & geometric helpers
├── tools/              # Categorized tool definitions
│   ├── geocoding.py
│   ├── routing.py
│   ├── search.py
│   └── analysis.py
└── resources.py        # Map & Data resources

⚖️ 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.

from github.com/neco001/openstreetmap-mcp

Installing Openstreetmap

This server has no published package — it is built from source. Open the repository and follow its README.

▸ github.com/neco001/openstreetmap-mcp

FAQ

Is Openstreetmap MCP free?

Yes, Openstreetmap MCP is free — one-click install via Unyly at no cost.

Does Openstreetmap need an API key?

No, Openstreetmap runs without API keys or environment variables.

Is Openstreetmap hosted or self-hosted?

Self-hosted: the server runs locally on your machine via the install command above.

How do I install Openstreetmap in Claude Desktop, Claude Code or Cursor?

Open Openstreetmap on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

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