Provides support for reading and writing ESRI Shapefiles and includes a roadmap for full ESRI FileGDB integration, ensuring compatibility with standard GIS data formats.
Utilizes OpenStreetMap data through services like Nominatim and OSRM to provide geocoding, reverse geocoding, and routing tools for spatial analysis and navigation.
Click on "Install 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., "@LocuSync Server calculate the driving route and travel time from Seattle to Portland"
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.
A Model Context Protocol (MCP) server providing geospatial tools for AI agents. Enables Claude, GPT, and other LLMs to perform geocoding, routing, spatial analysis, and file operations.
Geocoding: Convert addresses to coordinates and vice versa (via Nominatim/OSM or Pelias)
Batch Geocoding: Geocode multiple addresses in a single request (up to 10)
Elevation Data: Get altitude for points and elevation profiles along paths
Routing: Calculate routes between points with distance, duration, and geometry (via OSRM)
Spatial Analysis: Buffer, intersection, union, distance calculations
File I/O: Read/write Shapefiles, GeoJSON, GeoPackage
CRS Transformation: Convert between coordinate reference systems
Related MCP server: GDAL MCP
# From PyPI (when published)
pip install locusync-server
# From source
git clone https://github.com/matbel91765/locusync-server.git
cd locusync-server
pip install -e .Add to your claude_desktop_config.json:
{
"mcpServers": {
"locusync": {
"command": "uvx",
"args": ["locusync-server"]
}
}
}# Run the server
locusync-servergeocodeConvert an address to coordinates.
Input: "1600 Pennsylvania Avenue, Washington DC"
Output: {lat: 38.8977, lon: -77.0365, display_name: "White House..."}reverse_geocodeConvert coordinates to an address.
Input: lat=48.8566, lon=2.3522
Output: {display_name: "Paris, Île-de-France, France", ...}batch_geocodeGeocode multiple addresses at once (max 10).
Input: addresses=["Paris, France", "London, UK", "Berlin, Germany"]
Output: {results: [...], summary: {total: 3, successful: 3, failed: 0}}get_elevationGet altitude for a point.
Input: lat=48.8566, lon=2.3522
Output: {elevation_m: 35, location: {lat: 48.8566, lon: 2.3522}}get_elevation_profileGet elevations along a path.
Input: coordinates=[[2.3522, 48.8566], [2.2945, 48.8584]]
Output: {profile: [...], stats: {min: 28, max: 42, gain: 14}}distanceCalculate distance between two points.
Input: lat1=48.8566, lon1=2.3522, lat2=51.5074, lon2=-0.1278
Output: {distance: {meters: 343556, kilometers: 343.56, miles: 213.47}}bufferCreate a buffer zone around a geometry.
Input: geometry={type: "Point", coordinates: [2.3522, 48.8566]}, distance_meters=1000
Output: {geometry: {type: "Polygon", ...}, area_km2: 3.14}spatial_queryPerform spatial operations (intersection, union, contains, within, etc.).
Input: geometry1={...}, geometry2={...}, operation="intersection"
Output: {geometry: {...}}transform_crsTransform coordinates between CRS.
Input: geometry={...}, source_crs="EPSG:4326", target_crs="EPSG:3857"
Output: {geometry: {...}}routeCalculate route between two points.
Input: start_lat=48.8566, start_lon=2.3522, end_lat=48.8606, end_lon=2.3376
Output: {distance: {...}, duration: {...}, geometry: {...}, steps: [...]}isochroneCalculate area reachable within a time limit.
Input: lat=48.8566, lon=2.3522, time_minutes=15, profile="driving"
Output: {geometry: {type: "Polygon", ...}}read_fileRead geospatial files (Shapefile, GeoJSON, GeoPackage).
Input: file_path="data/cities.shp"
Output: {type: "FeatureCollection", features: [...]}write_fileWrite features to geospatial files.
Input: features={...}, file_path="output.geojson", driver="GeoJSON"
Output: {file_path: "...", feature_count: 10}Environment variables:
Variable | Default | Description |
|
| Nominatim API URL |
|
| User agent for Nominatim |
|
| OSRM API URL |
|
| Default routing profile |
| (empty) | Pelias geocoding API URL |
| (empty) | Pelias API key (optional) |
|
| Open-Elevation API URL |
|
| Default CRS |
|
| Temporary directory |
All tools return a consistent JSON structure:
{
"success": true,
"data": { ... },
"metadata": {
"source": "nominatim",
"confidence": 0.95
},
"error": null
}Nominatim: 1 request/second (enforced automatically)
OSRM Demo: Best effort, consider self-hosting for production
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Run with coverage
pytest --cov=src/locusync --cov-report=html
# Type checking
mypy src/locusync
# Linting
ruff check src/locusyncsrc/locusync/
├── server.py # MCP server entry point
├── config.py # Configuration management
├── utils.py # Common utilities
└── tools/
├── geocoding.py # geocode, reverse_geocode, batch_geocode
├── elevation.py # get_elevation, get_elevation_profile
├── routing.py # route, isochrone
├── geometry.py # buffer, distance, spatial_query, transform_crs
└── files.py # read_file, write_fileMIT License - see LICENSE for details.
Contributions welcome! Please read the contributing guidelines before submitting PRs.
Pelias geocoding support (higher accuracy)
Elevation/terrain data
Batch geocoding
Valhalla routing integration (native isochrones)
PostGIS spatial queries
Real-time traffic data
ESRI FileGDB full support
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
MCP server for Mireye Earth — federal-source-cited geospatial data for any MCP-aware agent.
Geospatial MCP server for earthquake, tsunami, volcano, disaster, and FX data queries.
Autopilot MCP server for GEO analyses, reports, content, audits, memories and agents.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/matbel91765/GIS-MCP-Server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server