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Gis MCP

by mahdin75

Connects AI assistants to GIS libraries for geospatial analysis, transformations, raster and vector processing, and spatial statistics.

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Install Gis MCP

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "mahdin75-gis-mcp": {
      "command": "uvx",
      "args": [
        "gis-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Automated check passed— started and listed 87 tools correctly (18d ago).

Overview

The mahdin75/gis-mcp MCP server exposes GIS and geospatial operations to AI assistants through MCP-compatible clients. It provides more than 92 tools spanning Shapely, GeoPandas, PyProj, Rasterio, PySAL, visualization, and several geospatial data sources. Use it when an agent needs spatial analysis, vector or raster processing, coordinate transformations, mapping, or related GIS workflows over HTTP or stdio.

Use cases

  • Analyze vector and raster geospatial data
  • Transform coordinates between spatial reference systems
  • Calculate spatial statistics
  • Gather satellite, climate, ecology, or land-cover data
  • Build geospatial agents with LangChain or OpenAI

Key features

  • 92+ GIS tools
  • Shapely and GeoPandas vector operations
  • Rasterio raster processing
  • PyProj coordinate transformations
  • PySAL spatial statistics
  • HTTP and stdio transports

Tools87

What Gis MCP lets your agent do. Expand a tool for its parameters.

Verified live~8.4k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

21/21 checks healthy over the last 78d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 7d ago via OSV.dev · gis-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars199Stargazers on the source repository.
Last commit2mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed87Callable tools this server registers over MCP.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How Gis MCP works

What mahdin75/gis-mcp does

The mahdin75/gis-mcp MCP server connects large language models and AI assistants with GIS libraries and geospatial workflows. Its toolset covers spatial analysis, data processing, coordinate transformations, spatial statistics, visualization, and mapping. The project describes more than 92 tools across established Python GIS packages and several domain-specific data areas.

Supported capability areas include vector operations through Shapely and GeoPandas, coordinate work through PyProj, raster processing through Rasterio, and spatial statistics through PySAL. The server also lists satellite imagery, climate, ecology, movement, land-cover, biodiversity, and administrative-boundary data sources.

How it works

An MCP-compatible client connects to the server and presents its GIS operations as callable agent tools. The repository advertises both HTTP and stdio transport, allowing the deployment style to match the client or environment. Agents can use the exposed operations for geospatial analysis and transformations rather than treating GIS work as text-only reasoning.

The project includes examples for building geospatial agents with LangChain in Python and the OpenAI Agents SDK in Node.js. These examples indicate that the server can be used either from an MCP client or as part of an agent application that supports MCP connections.

Setup and configuration

The package is published on PyPI as gis-mcp, and the project also provides Docker installation documentation. Deployment can use either the advertised HTTP or stdio transport. Storage documentation covers a local filesystem option and Google Cloud Storage.

Consult the project documentation for those details before deploying a particular storage backend or transport.

Tools and capabilities

The mahdin75/gis-mcp MCP server groups its tools across the following areas:

  • Vector operations using Shapely and GeoPandas
  • Raster operations using Rasterio
  • Coordinate transformations using PyProj
  • Spatial statistics using PySAL
  • Visualization and mapping
  • Satellite imagery access
  • Climate, ecology, biodiversity, movement, and land-cover data
  • Administrative-boundary data

This combination fits workflows that need both general-purpose geometry processing and domain-specific geospatial data gathering.

Limitations and notes

The available description does not enumerate every tool name, input schema, output format, supported coordinate reference systems, or data-provider requirements. It also does not establish performance characteristics or production-readiness guarantees. Treat the beta status as relevant when evaluating the server for critical workloads, and verify the official documentation for exact operation behavior and configuration requirements.

The listed storage choices are local filesystem and Google Cloud Storage.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Gis MCP

mahdin75/gis-mcp is an MCP server that connects AI assistants and large language models to GIS libraries and geospatial data workflows. Its main tools cover vector and raster operations, coordinate transformations, spatial statistics, visualization, mapping, satellite imagery, climate, ecology, movement, and land-cover data.

Install it from PyPI or use the documented Docker installation. The package is published as `gis-mcp`, and the project supports HTTP and stdio transports. Consult the official installation documentation for the exact command and client configuration required for your deployment.

Yes, it can be used with Cursor as an MCP-compatible client.

It supports local filesystem storage and Google Cloud Storage.

Yes, the project advertises both HTTP and stdio transport. The appropriate choice depends on the MCP client and deployment environment, while exact startup commands and settings are provided in the project documentation.

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