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Gis MCP

A Model Context Protocol (MCP) server implementation that connects Large Language Models (LLMs) to GIS operations using GIS libraries, enabling AI assistants to perform geospatial operations and transformations.

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GIS MCP Server

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Installation & Transport
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Agentic AI
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✨ Want to perform accurate geospatial analysis in your chatbot? ✨

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

🌐 Website: gis-mcp.com

Current version is 0.14.0 (Beta):

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

🎥 Demo

🚀 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 & 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.

📋 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, use these context files in your editor so the LLM understands the GIS MCP server:

llms.txt: summarized context for smaller windows.
llms-full.txt: full context when your model has a larger window.

🛠 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:

Build the Docker image:

docker build -t gis-mcp .

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):

Build the Docker image:

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

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:

Install uv package manager:

pip install uv

Create the Virtual Environment (Python 3.10+):

uv venv --python=3.10

Activate the Virtual Environment:

On Windows (PowerShell):

.\.venv\Scripts\Activate.ps1

On Linux:

source .venv/bin/activate

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

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:

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:

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

🛠 Development Installation

For contributors and developers:

Install uv package manager:

pip install uv

Create the Virtual Environment:

uv venv --python=3.10

Install the package in development mode:

uv pip install -e .

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:

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

🤖 Build Your First GIS AI Agent

Ready to create your own AI agent that can perform geospatial operations? Our comprehensive tutorial will guide you from zero to hero!

What You'll Learn

✅ How to set up the GIS MCP server in HTTP mode
✅ How to build a LangChain agent from scratch
✅ How to connect your agent to GIS tools
✅ How to use OpenRouter to access multiple AI models (DeepSeek, Gemini, GPT-4, Claude, etc.)
✅ How to customize and extend your agent

Get Started

👉 Follow the complete tutorial →

📝 Read the Medium article →

🎥 Watch tutorials on YouTube →

The tutorial is beginner-friendly and requires no prior AI or GIS experience. You'll build a working agent that can:

Calculate distances between points
Transform coordinates between different systems
Create buffers around locations
Perform spatial analysis
And much more!

Perfect for: Developers, data scientists, GIS professionals, and anyone interested in building AI-powered geospatial applications.

📚 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:

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
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

Project Name	Category	Description
Model Context Protocol	MCP Related	The core MCP Specification
FastMCP	MCP Related	The fast, Pythonic way to build MCP servers and clients
Shapely	Geospatial Analysis	Python package for manipulation and analysis of geometric objects
PyProj	Geospatial Analysis	Python interface to PROJ library
GeoPandas	Geospatial Analysis	Python package for working with geospatial data
Rasterio	Geospatial Analysis	Python package for reading and writing geospatial raster data
Fiona	Geospatial Analysis	Python interface to OGR library for reading and writing vector geospatial data formats
PySAL	Geospatial Analysis	Python spatial analysis library for geospatial data science
Folium	Visualization	Python library for creating interactive web maps using Leaflet.js
PyDeck	Visualization	Python library for creating advanced 3D visualizations and interactive maps
Matplotlib	Visualization	Python plotting library for creating static maps and visualizations
cdsapi	Geospatial Data Collecting	Python API to access the Copernicus Climate Data Store (CDS)
pygadm	Geospatial Data Collecting	Easy access to administrative boundary defined by GADM from Python scripts
pygbif	Geospatial Data Collecting	Python client for the GBIF API (ecology and biodiversity data)
OSMnx	Geospatial Data Collecting	Python package for downloading, modeling, and analyzing street networks and urban features from OpenStreetMap
pystac-client	Geospatial Data Collecting	Python client for STAC catalogs; search and access spatiotemporal assets
Planetary Computer SDK for Python	Geospatial Data Collecting	Python SDK for Microsoft Planetary Computer; auth and helpers for STAC/COGs

🔗 Related MCP Servers

Server Name	Description
GeoServer MCP	A Model Context Protocol (MCP) server implementation that connects LLMs to the GeoServer REST API

📞 Support

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

💬 Community

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

👥 Contributors

Made with contrib.rocks.

Gis MCP

Installation

Reviews

Documentation

GIS MCP Server

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

🎥 Demo

📋 Table of Contents

🚀 Features

📋 Prerequisites

Vibe Coding

🛠 Installation

🐳 Docker Installation

Using Dockerfile

Using Dockerfile.local

📦 pip Installation

Install with Visualization Features

pip Configuration

🛠 Development Installation

Development Configuration

🤖 Build Your First GIS AI Agent

What You'll Learn

Get Started

📚 Available Functions

🔷 Shapely Functions (29 total)

🔷 PyProj Functions (13 total)

🔷 GeoPandas Functions (13 total)

🔷 Rasterio Functions (20 total)

🔷 PySAL Functions (18 total)

🔷 Visualization Functions (2 total)

🔷 Administrative Boundaries Functions (1 total)

🔷 Climate Data Functions (1 total)

🔷 Ecology Data Functions (2 total)

🔷 Movement Data Functions (2 total)

🔷 Land Cover Data Functions (2 total)

🔷 Satellite Imagery Functions (1 total)

🛠 Client Development

Buffer Operation

Coordinate Transformation

Geodetic Distance

Static Map Creation

Interactive Web Map Creation

🔮 Planned Features

🤝 Contributing

📄 License

🔗 Related Projects

🔗 Related MCP Servers

📞 Support

💬 Community

👥 Contributors

🏆 Badges