OpenStreetMap MCP Server: High-Performance, Scalable Mapping Solutions

Key Features of OpenStreetMap MCP Server: High-Performance, Scalable Mapping Solutions?

• Multi-Category Geocoding: Supports address-to-coordinate conversion with reverse geocoding for location identification
• Radius-based Search: Discovers amenities/services within user-defined geographic boundaries
• Route Optimization: Calculates shortest paths and travel times using OpenStreetMap routing algorithms
• Scalable Architecture: Handles concurrent requests with load-balanced API endpoints
• Compliance-aware: Incorporates licensing frameworks for OpenStreetMap data usage

FAQ: OpenStreetMap MCP Server

OpenStreetMap MCP Server

An OpenStreetMap MCP server implementation that enhances LLM capabilities with location-based services and geospatial data.

Demo

Meeting Point Optimization

Neighborhood Analysis

Parking Search

Features

This server provides LLMs with tools to interact with OpenStreetMap data, enabling location-based applications to:

• Geocode addresses and place names to coordinates
• Reverse geocode coordinates to addresses
• Find nearby points of interest
• Get route directions between locations
• Search for places by category within a bounding box
• Suggest optimal meeting points for multiple people
• Explore areas and get comprehensive location information
• Find schools and educational institutions near a location
• Analyze commute options between home and work
• Locate EV charging stations with connector and power filtering
• Perform neighborhood livability analysis for real estate
• Find parking facilities with availability and fee information

Components

Resources

The server implements location-based resources:

• location://place/{query}: Get information about places by name or address
• location://map/{style}/{z}/{x}/{y}: Get styled map tiles at specified coordinates

Tools

The server implements several geospatial tools:

• geocode_address: Convert text to geographic coordinates
• reverse_geocode: Convert coordinates to human-readable addresses
• find_nearby_places: Discover points of interest near a location
• get_route_directions: Get turn-by-turn directions between locations
• search_category: Find places of specific categories in an area
• suggest_meeting_point: Find optimal meeting spots for multiple people
• explore_area: Get comprehensive data about a neighborhood
• find_schools_nearby: Locate educational institutions near a specific location
• analyze_commute: Compare transportation options between home and work
• find_ev_charging_stations: Locate EV charging infrastructure with filtering
• analyze_neighborhood: Evaluate neighborhood livability for real estate
• find_parking_facilities: Locate parking options near a destination

Use Cases

Real Estate Decision Making

An LLM can help users evaluate potential neighborhoods for home purchases:

Local Testing

Running the Server

To run the server locally:

1. Install the package in development mode:

pip install -e .

2. Start the server:

osm-mcp-server

3. The server will start and listen for MCP requests on the standard input/output.

Testing with Example Clients

The repository includes two example clients in the examples/ directory:

Basic Client Example

client.py demonstrates basic usage of the OSM MCP server:

python examples/client.py

This will:

• Connect to the locally running server
• Get information about San Francisco
• Search for restaurants in the area
• Retrieve comprehensive map data with progress tracking

LLM Integration Example

llm_client.py provides a helper class designed for LLM integration:

python examples/llm_client.py

This example shows how an LLM can use the Location Assistant to:

• Get location information from text queries
• Find nearby points of interest
• Get directions between locations
• Find optimal meeting points
• Explore neighborhoods

Writing Your Own Client

To create your own client:

1. Import the MCP client:

from mcp.client import Client

2. Initialize the client with your server URL:

client = Client("http://localhost:8000")

3. Invoke tools or access resources:

# Example: Geocode an address
results = await client.invoke_tool("geocode_address", {"address": "New York City"})

Configuration

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

"mcpServers": {
  "osm-mcp-server": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/osm-mcp-server",
      "run",
      "osm-mcp-server"
    ]
  }
}

"mcpServers": {
  "osm-mcp-server": {
    "command": "uvx",
    "args": [
      "osm-mcp-server"
    ]
  }
}

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags.

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /path/to/osm-mcp-server run osm-mcp-server

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Example API Usage

Here's a quick example of how to use the key API endpoints from Python code:

import asyncio
from mcp.client import Client

async def main():
    client = Client("http://localhost:8000")
    
    # Geocode an address
    results = await client.invoke_tool("geocode_address", {"address": "Empire State Building"})
    print(f"Found location: {results[0]['display_name']}")
    
    # Get coordinates
    lat = float(results[0]['lat'])
    lon = float(results[0]['lon'])
    
    # Find nearby coffee shops
    nearby = await client.invoke_tool(
        "find_nearby_places",
        {
            "latitude": lat,
            "longitude": lon,
            "radius": 500,
            "categories": ["amenity"],
            "limit": 5
        }
    )
    
    # Print results
    print(f"Found {nearby['total_count']} nearby places")
    for category, subcategories in nearby["categories"].items():
        for subcategory, places in subcategories.items():
            print(f"  - {subcategory}: {len(places)} places")

if __name__ == "__main__":
    asyncio.run(main())