GIS MCP Server

GIS-MCP is an open-source Model Context Protocol (MCP) server that connects AI assistants and Large Language Models (LLMs) with GIS and geospatial operations.

It enables AI agents to perform geospatial analysis, spatial data processing, vector and raster operations, coordinate transformations, spatial statistics, mapping, and other GIS workflows through MCP-compatible clients.

🌐 Official Documentation: https://gis-mcp.com

📦 PyPI: https://pypi.org/project/gis-mcp/

💻 Source Code: https://github.com/mahdin75/gis-mcp

92+ GIS tools across Shapely, GeoPandas, PyProj, Rasterio, PySAL, visualization, satellite imagery, climate, ecology, movement, and land-cover data.

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✨ Want to perform accurate geospatial analysis in your chatbot? ✨

Install GIS-MCP and transform your AI's spatial capabilities!

Current version is 0.15.0 (Beta):

We welcome contributions and developers to join us in building this project.

🎥 Demo

📋 Table of Contents

• Features
• Storage
• Prerequisites
• Vibe Coding
• Installation
  • Docker Installation
  • pip Installation
  • Development Installation
• Build Your First GIS AI Agent
• Available Functions
  • Shapely Functions
  • PyProj Functions
  • GeoPandas Functions
  • Rasterio Functions
  • PySAL Functions
  • Visualization Functions
    • Static Map Functions
    • Interactive Web Map Functions
  • Administrative Boundaries Functions
  • Climate Data Functions
  • Ecology Data Functions
  • Movement Data Functions
  • Land Cover Data Functions
  • Satellite Imagery Functions
• Client Development
• Planned Features
• Contributing
• License
• Related Projects
• Support
• Badges

🚀 Features

GIS MCP Server empowers AI assistants with advanced geospatial intelligence. Key features include:

• 🔹 Comprehensive Geometry Operations – Perform intersection, union, buffer, difference, and other geometric transformations with ease.
• 🔹 Advanced Coordinate Transformations – Effortlessly reproject and transform geometries between coordinate reference systems.
• 🔹 Accurate Measurements – Compute distances, areas, lengths, and centroids precisely.
• 🔹 Spatial Analysis & Validation – Validate geometries, run proximity checks, and perform spatial overlays or joins.
• 🔹 Raster & Vector Support – Process raster layers, compute indices like NDVI, clip, resample, and merge with vector data.
• 🔹 Spatial Statistics & Modeling – Leverage PySAL for spatial autocorrelation, clustering, and neighborhood analysis.
• 🔹 Easy Integration – Connect seamlessly with MCP-compatible clients like Claude Desktop or Cursor IDE.
• 🔹 HTTP/SSE Transport – Run as HTTP service with RESTful storage endpoints for file upload/download operations.
• 🔹 Flexible Storage – Local filesystem by default, or Google Cloud Storage when you want files in a GCS bucket.
• 🔹 Flexible & Extensible – Supports Python-based GIS libraries and is ready for custom tools or workflow extensions.

🌟 Tip: With GIS MCP Server, your AI can now “think spatially,” unlocking new capabilities for environmental analysis, mapping, and location intelligence.

💾 Storage

GIS MCP Server supports two storage backends for file upload, download, and tool outputs:

Install GCP support with:

pip install gis-mcp[gcp]

Full setup (paths, IAM, Docker, credentials): Storage Configuration.

📋 Prerequisites

• Python 3.10 or higher
• MCP-compatible client (like Claude Desktop or Cursor)
• Internet connection for package installation

Vibe Coding

If you’re building agents via vibe coding, give the model these generated maps of GIS MCP:

• llms.txt: curated summary for smaller windows.
• llms-full.txt: full documentation map for larger windows.

Copies also live in the repository root (llms.txt, llms-full.txt). They are regenerated from the docs during the MkDocs build.

🛠 Installation

Choose the installation method that best suits your needs:

🐳 Docker Installation

GIS MCP Server can be run using Docker, which provides an isolated environment with all dependencies pre-installed.

Important: Both Dockerfile and Dockerfile.local have HTTP transport mode enabled by default. The server runs on port 9010 and is accessible at http://localhost:9010/mcp.

Using Dockerfile

The main Dockerfile installs the package from PyPI:

1. Build the Docker image:

docker build -t gis-mcp .

2. Run the container (HTTP mode is enabled by default):

docker run -p 9010:9010 gis-mcp

Using Dockerfile.local

The Dockerfile.local installs the package from local source files (useful for development or custom builds):

1. Build the Docker image:

docker build -f Dockerfile.local -t gis-mcp:local .

2. Run the container (HTTP mode is enabled by default):

docker run -p 9010:9010 gis-mcp:local

The server will be available at http://localhost:9010/mcp in HTTP transport mode.

For more details on Docker configuration and environment variables, see the Docker installation guide.

📦 pip Installation

The pip installation is recommended for most users:

1. Install uv package manager:

pip install uv

2. Create the Virtual Environment (Python 3.10+):

uv venv --python=3.10

3. Activate the Virtual Environment:

On Windows (PowerShell):

.\.venv\Scripts\Activate.ps1

On Linux:

source .venv/bin/activate

4. Install the package:

uv pip install gis-mcp

Install with Visualization Features

To install with visualization capabilities (Folium and PyDeck for interactive maps):

uv pip install gis-mcp[visualize]

This will install additional dependencies:

• folium>=0.15.0 - For creating interactive web maps
• pydeck>=0.9.0 - For advanced 3D visualizations

5. Start the server:

gis-mcp

By default, the server runs in STDIO transport mode, which is ideal for local development and integration with Claude Desktop or Cursor IDE.

You can also run the server in HTTP transport mode for network deployments:

export GIS_MCP_TRANSPORT=http
export GIS_MCP_PORT=8080
gis-mcp

When running in HTTP or SSE transport mode, the following endpoints are available:

• MCP endpoint: http://host:port/mcp (HTTP) or http://host:port/sse (SSE)
• Storage endpoints:
  • POST /storage/upload - Upload files to server storage
  • GET /storage/download?path=<file> - Download files from server storage
  • GET /storage/list?path=<dir> - List files in server storage

For more details on transport modes and complete endpoint documentation, see:

• HTTP Transport Configuration
• Server Endpoints

pip Configuration

To use the pip installation with Claude or Cursor, add the following configuration:

Claude Desktop:

Windows:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "C:\\Users\\YourUsername\\.venv\\Scripts\\gis-mcp",
      "args": []
    }
  }
}

Linux/Mac:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "/home/YourUsername/.venv/bin/gis-mcp",
      "args": []
    }
  }
}

Cursor IDE (create .cursor/mcp.json):

Windows:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "C:\\Users\\YourUsername\\.venv\\Scripts\\gis-mcp",
      "args": []
    }
  }
}

Linux/Mac:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "/home/YourUsername/.venv/bin/gis-mcp",
      "args": []
    }
  }
}

After configuration:

1. Make sure to replace YourUsername with your actual username
2. For development installation, replace /path/to/gis-mcp with the actual path to your project
3. Restart your IDE to apply the changes
4. You can now use all GIS operations through Claude or Cursor!

🛠 Development Installation

For contributors and developers:

1. Install uv package manager:

pip install uv

2. Create the Virtual Environment:

uv venv --python=3.10

3. Install the package in development mode:

uv pip install -e .

4. Start the server:

python -m gis_mcp

Development Configuration

To use the development installation with Claude or Cursor, add the following configuration:

Claude Desktop:

Windows:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "C:\\path\\to\\gis-mcp\\.venv\\Scripts\\python",
      "args": ["-m", "gis_mcp"]
    }
  }
}

Linux/Mac:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "/path/to/gis-mcp/.venv/bin/python",
      "args": ["-m", "gis_mcp"]
    }
  }
}

Cursor IDE (create .cursor/mcp.json):

Windows:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "C:\\path\\to\\gis-mcp\\.venv\\Scripts\\python",
      "args": ["-m", "gis_mcp"]
    }
  }
}

Linux/Mac:

{
  "mcpServers": {
    "gis-mcp": {
      "command": "/path/to/gis-mcp/.venv/bin/python",
      "args": ["-m", "gis_mcp"]
    }
  }
}

After configuration:

1. Make sure to replace YourUsername with your actual username
2. For development installation, replace /path/to/gis-mcp with the actual path to your project
3. Restart your IDE to apply the changes
4. You can now use all GIS operations through Claude or Cursor!

🤖 Build Your First GIS AI Agent

Create agents that call real GIS MCP tools (not invented spatial math).

LangChain (Python) — park buffer proximity

Tutorial: Park buffer proximity agent

Sample: agents/Langchain/

LangGraph (Python) — stateful site coverage

Tutorial: Stateful site-coverage workflow

Sample: agents/LangGraph/

• Explicit graph: interpret → plan → execute GIS MCP tools → validate → respond
• Verify without an LLM: python agents/LangGraph/verify_graph.py

LangGraph (Python) — multi-agent setback

Tutorial: Multi-agent setback workflow

• Planner / Analysis / Validation agents; only Analysis calls GIS MCP
• Verify: python agents/LangGraph/verify_multi_agent.py

More agent docs

👉 Agent tutorials overview →

Minimal starters: templates/ (langchain-gis-agent, langgraph-gis-workflow, langgraph-multi-agent-gis)

Also available: OpenAI Agents SDK (Node.js)

📝 Medium article (LangChain intro) →

🎥 YouTube →

📚 Available Functions

This section provides a comprehensive list of all available functions organized by library.

🔷 Shapely Functions (29 total)

Basic Geometric Operations:

• buffer - Create buffer around geometry
• intersection - Find intersection of two geometries
• union - Combine two geometries
• difference - Find difference between geometries
• symmetric_difference - Find symmetric difference

Geometric Properties:

• convex_hull - Calculate convex hull
• envelope - Get bounding box
• minimum_rotated_rectangle - Get minimum rotated rectangle
• get_centroid - Get centroid point
• get_bounds - Get geometry bounds
• get_coordinates - Extract coordinate array
• get_geometry_type - Get geometry type name

Transformations:

• rotate_geometry - Rotate geometry by angle
• scale_geometry - Scale geometry by factors
• translate_geometry - Move geometry by offset

Advanced Operations:

• triangulate_geometry - Create triangulation
• voronoi - Create Voronoi diagram
• unary_union_geometries - Union multiple geometries

Measurements:

• get_length - Calculate geometry length
• get_area - Calculate geometry area

Validation & Utilities:

• is_valid - Check geometry validity
• make_valid - Fix invalid geometry
• simplify - Simplify geometry
• snap_geometry - Snap to reference geometry
• nearest_point_on_geometry - Find nearest point
• normalize_geometry - Normalize orientation
• geometry_to_geojson - Convert to GeoJSON
• geojson_to_geometry - Convert from GeoJSON

🔷 PyProj Functions (13 total)

Coordinate Transformations:

• transform_coordinates - Transform point coordinates
• project_geometry - Project geometry between CRS

CRS Information:

• get_crs_info - Get detailed CRS information
• get_available_crs - List available CRS systems
• get_utm_zone - Get UTM zone for coordinates
• get_utm_crs - Get UTM CRS for coordinates
• get_geocentric_crs - Get geocentric CRS

Geodetic Calculations:

• get_geod_info - Get ellipsoid information
• calculate_geodetic_distance - Calculate distance on ellipsoid
• calculate_geodetic_point - Calculate point at distance/azimuth
• calculate_geodetic_area - Calculate area on ellipsoid

🔷 GeoPandas Functions (13 total)

I/O Operations:

• read_file_gpd - Read geospatial file with preview
• write_file_gpd - Export GeoDataFrame to file

Join & Merge Operations:

• append_gpd - Concatenate GeoDataFrames vertically
• merge_gpd - Database-style attribute joins
• overlay_gpd - Spatial overlay operations
• dissolve_gpd - Dissolve by attribute
• explode_gpd - Split multi-part geometries

Spatial Operations:

• clip_vector - Clip geometries
• sjoin_gpd - Spatial joins
• sjoin_nearest_gpd - Nearest neighbor spatial joins
• point_in_polygon - Point-in-polygon tests

🔷 Rasterio Functions (20 total)

Basic Raster Operations:

• metadata_raster - Get raster metadata
• get_raster_crs - Get raster CRS
• extract_band - Extract single band
• raster_band_statistics - Calculate band statistics
• raster_histogram - Compute pixel histograms

Raster Processing:

• clip_raster_with_shapefile - Clip raster with polygons
• resample_raster - Resample by scale factor
• reproject_raster - Reproject to new CRS
• tile_raster - Split into tiles

Raster Analysis:

• compute_ndvi - Calculate vegetation index
• raster_algebra - Mathematical operations on bands
• concat_bands - Combine single-band rasters
• weighted_band_sum - Weighted band combination

Advanced Analysis:

• zonal_statistics - Statistics within polygons
• reclassify_raster - Reclassify pixel values
• focal_statistics - Moving window statistics
• hillshade - Generate hillshade from DEM
• write_raster - Write array to raster file

🔷 PySAL Functions (18 total)

Spatial Autocorrelation:

• morans_i - Global Moran's I statistic
• gearys_c - Global Geary's C statistic
• gamma_statistic - Gamma index
• getis_ord_g - Global Getis-Ord G statistic

Local Statistics:

• moran_local - Local Moran's I
• getis_ord_g_local - Local Getis-Ord G*
• join_counts_local - Local join counts

Global Statistics:

• join_counts - Binary join counts test
• adbscan - Adaptive density-based clustering

Spatial Weights:

• weights_from_shapefile - Create weights from shapefile
• distance_band_weights - Distance-based weights
• knn_weights - K-nearest neighbors weights
• build_transform_and_save_weights - Build, transform, and save weights
• ols_with_spatial_diagnostics_safe - OLS regression with spatial diagnostics
• build_and_transform_weights - Build and transform weights

Spatial-Temporal Analysis:

• spatial_markov - Spatial Markov analysis for panel data
• dynamic_lisa - Dynamic LISA (directional LISA) analysis

Spatial Regression:

• gm_lag - GM_Lag spatial 2SLS/GMM-IV spatial lag model

🔷 Visualization Functions (2 total)

Static Map Visualization (Matplotlib/GeoPandas):

• create_map – Generate high-quality static maps (PNG, PDF, JPG) from multiple geospatial data sources including shapefiles, rasters, WKT geometries, and coordinate arrays. Supports multiple layers with individual styling options, legends, titles, and grid overlays.

Interactive Web Map Visualization (Folium):

• create_web_map – Generate interactive HTML maps using Folium with layer controls, legends, scale bars, dynamic titles, tooltips, and minimap. Supports multiple basemap options and responsive design for web browsers.

🔷 Administrative Boundaries Functions (1 total)

Boundary Download:

• download_boundaries - Download GADM administrative boundaries and save as GeoJSON

🔷 Climate Data Functions (1 total)

Climate Data Download:

• download_climate_data - Download climate data (ERA5 or other CDS datasets)

🔷 Ecology Data Functions (2 total)

Ecology Data Download and Info:

• get_species_info – Retrieve taxonomic information for a given species name
• download_species_occurrences – Download occurrence records for a given species and save as JSON

🔷 Movement Data Functions (2 total)

Movement Data Download and Routing (via OSMnx):

• download_street_network – Download a street network for a given place and save as GraphML
• calculate_shortest_path – Calculate the shortest path between two points using a saved street network

🔷 Land Cover Data Functions (2 total)

Land Cover from Planetary Computer:

• download_worldcover – Download ESA WorldCover for AOI/year; optional crop and reprojection
• compute_s2_ndvi – Compute NDVI from Sentinel-2 L2A; crop and reprojection supported

🔷 Satellite Imagery Functions (1 total)

STAC-based Satellite Download:

• download_satellite_imagery – Download and stack bands from STAC items (e.g., Sentinel-2, Landsat), with optional crop and reprojection

Total Functions Available: 92

🛠 Client Development

Example usage of the tools:

Buffer Operation

Tool: buffer
Parameters: {
    "geometry": "POINT(0 0)",
    "distance": 10,
    "resolution": 16,
    "join_style": 1,
    "mitre_limit": 5.0,
    "single_sided": false
}

Coordinate Transformation

Tool: transform_coordinates
Parameters: {
    "coordinates": [0, 0],
    "source_crs": "EPSG:4326",
    "target_crs": "EPSG:3857"
}

Geodetic Distance

Tool: calculate_geodetic_distance
Parameters: {
    "point1": [0, 0],
    "point2": [10, 10],
    "ellps": "WGS84"
}

Static Map Creation

Tool: create_map
Parameters: {
    "layers": [
        {
            "data": "buildings.shp",
            "style": {"label": "Buildings", "color": "red", "alpha": 0.7}
        },
        {
            "data": "roads.shp",
            "style": {"label": "Roads", "color": "black", "linewidth": 1}
        }
    ],
    "filename": "city_analysis",
    "filetype": "png",
    "title": "City Infrastructure Analysis",
    "show_grid": true,
    "add_legend": true
}

Interactive Web Map Creation

Tool: create_web_map
Parameters: {
    "layers": [
        {
            "data": "buildings.shp",
            "style": {"label": "Buildings", "color": "red"}
        },
        {
            "data": "parks.geojson",
            "style": {"label": "Parks", "color": "green"}
        }
    ],
    "filename": "city_interactive.html",
    "title": "City Infrastructure Map",
    "basemap": "CartoDB positron",
    "show_grid": true,
    "add_legend": true,
    "add_minimap": true
}

🔮 Planned Features

• Implement advanced spatial indexing
• Implement network analysis capabilities
• Add support for 3D geometries
• Implement performance optimizations
• Add support for more GIS libraries

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please ensure your PR description clearly describes the problem and solution. Include the relevant issue number if applicable.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Related Projects

🔗 Related MCP Servers

📞 Support

For support, please open an issue in the GitHub repository.

💬 Community

Join our Discord community for discussions, updates, and support:

👥 Contributors

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