Gis

GIS MCP Server

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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.

🌐 Website: gis-mcp.com

Current version is 0.14.0 (Beta):

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

🎥 Demo

📋 Table of Contents

• Features
• 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 & 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:

1. Build the Docker image:

docker build -t gis-mcp .

1. 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 .

1. 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 ins

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