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Gis

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

location-servicesaillm
By mahdin75
17445Updated 2 days agoPythonMIT

Installation

npx -y gis-mcp

Configuration

{
  "mcpServers": {
    "gis-mcp": {
      "command": "npx",
      "args": ["-y", "gis-mcp"]
    }
  }
}

How to use

  1. Run the installation command above (if needed)
  2. Open your Claude Code settings file (~/.claude/settings.json)
  3. Add the configuration to the mcpServers section
  4. Restart Claude Code to apply changes

README

View on GitHub

GIS MCP Server

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</div> <div align="center"> <h3>✨ Want to perform accurate geospatial analysis in your chatbot? ✨</h3> <p><strong>Install GIS-MCP and transform your AI's spatial capabilities!</strong></p> <br/> <img src="docs/Logo.png" alt="GIS MCP Server Logo" width="300"/> <br/> </div>

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.

How it fits next to the LLM and other tools, plus internal components: Architecture.

🌐 Website: gis-mcp.com

Current version is 0.15.0 (Beta):

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

🎥 Demo

<div align="center"> <img src="docs/demo.gif" alt="GIS MCP Server Demo" width="800"/> </div>

📋 Table of Contents

🚀 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:

BackendWhen to useConfigure with
Local filesystemDefault; files on disk (including Docker volumes)--storage-path or GIS_MCP_STORAGE_PATH
**Goog

…

View source on GitHub

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