OpenStreetMap (OSM) MCP Server
An OpenStreetMap MCP server implementation that enhances LLM capabilities with location-based services and geospatial data.
Demo
Meeting Point Optimization
Meeting Point Use Case
Neighborhood Analysis
Neighborhood Analysis Use Case
Parking Search
Parking Search Use Case
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:
- Install the package in development mode:
- Start the server:
- 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: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: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:
- Import the MCP client:
- Initialize the client with your server URL:
- Invoke tools or access resources:
Configuration
Install
Claude Desktop
On MacOS:
~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.jsonDevelopment
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
- Build package distributions:
This will create source and wheel distributions in the
dist/ directory.- Publish to PyPI:
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:
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:
OpenStreetMap (OSM) MCP Server
An OpenStreetMap MCP server implementation that enhances LLM capabilities with location-based services and geospatial data.
Demo
Meeting Point Optimization
Meeting Point Use Case
Neighborhood Analysis
Neighborhood Analysis Use Case
Parking Search
Parking Search Use Case
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:
- Install the package in development mode:
- Start the server:
- 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: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: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:
- Import the MCP client:
- Initialize the client with your server URL:
- Invoke tools or access resources:
Configuration
Install
Claude Desktop
On MacOS:
~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.jsonDevelopment
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
- Build package distributions:
This will create source and wheel distributions in the
dist/ directory.- Publish to PyPI:
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:
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: