https://github.com/jagan-shanmugam/open-streetmap-mcp
An OpenStreetMap MCP server implementation that enhances LLM capabilities with location-based services and geospatial data.
https://github.com/jagan-shanmugam/open-streetmap-mcp
anthropic-claude llm mcp mcp-server openstreetmap
Last synced: 25 days ago
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An OpenStreetMap MCP server implementation that enhances LLM capabilities with location-based services and geospatial data.
- Host: GitHub
- URL: https://github.com/jagan-shanmugam/open-streetmap-mcp
- Owner: jagan-shanmugam
- Created: 2025-03-03T21:17:47.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-27T17:29:50.000Z (25 days ago)
- Last Synced: 2025-03-27T18:31:12.155Z (25 days ago)
- Topics: anthropic-claude, llm, mcp, mcp-server, openstreetmap
- Language: Python
- Homepage:
- Size: 56 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-mcp-zh - jagan-shanmugam/open-streetmap-mcp
README
# OpenStreetMap (OSM) 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:
```bash
pip install -e .
```
2. Start the server:
```bash
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:
```bash
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:
```bash
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:
```python
from mcp.client import Client
```
2. Initialize the client with your server URL:
```python
client = Client("http://localhost:8000")
```
3. Invoke tools or access resources:
```python
# 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`
Development/Unpublished Servers Configuration
```json
"mcpServers": {
"osm-mcp-server": {
"command": "uv",
"args": [
"--directory",
"/path/to/osm-mcp-server",
"run",
"osm-mcp-server"
]
}
}
```
Published Servers Configuration
```json
"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:
```bash
uv sync
```
2. Build package distributions:
```bash
uv build
```
This will create source and wheel distributions in the `dist/` directory.
3. Publish to PyPI:
```bash
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](https://github.com/modelcontextprotocol/inspector).
You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:
```bash
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:
```python
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())
```