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  1. Home
  2. 🗺️ Location Services
  3. Geoserver MCP
Geoserver MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 10:16:44 PM

Geoserver MCP

User RatingsBe the first to rate and review this MCP server!
View Repository89 GitHub StarsTotal stargazers on GitHub for the source repository (89 stars).Visit Website
geoservergeospatialmcpogcdeveloper-tools

Connects MCP-compatible AI clients to GeoServer for REST administration, spatial queries, OGC services, and map generation.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "mahdin75-geoserver-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🗺️ More in Location Services

Overview

mahdin75/geoserver-mcp MCP server connects MCP-compatible AI clients to a running GeoServer instance through its REST API. It exposes operations for workspaces, stores, layers, styles, users, feature types, and service configuration, along with spatial queries and map visualization capabilities. The Python package runs locally through pip, Docker, or a client configuration, while a separate GeoServer extension targets GeoServer 2.28.x and provides an MCP endpoint. Reach for it when an agent needs to inspect or manage GeoServer resources rather than only read geospatial files.

Use cases

•Inspect GeoServer workspaces and layers
•Manage datastores, coveragestores, and styles
•Run spatial queries on vector data
•Generate map visualizations
•Administer GeoServer users and groups

Key features

•GeoServer REST API access over MCP
•Workspace, layer, and layer group management
•Datastore and coveragestore management
•Spatial queries on vector data
•WMS and WFS access
•Map visualization generation

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Geoserver MCP.

Extracted Tool Capabilities
GeoServer REST API access over MCP
Workspace, layer, and layer group management
Datastore and coveragestore management
Spatial queries on vector data
WMS and WFS access
Map visualization generation

How Geoserver MCP works

What mahdin75/geoserver-mcp MCP server does

mahdin75/geoserver-mcp MCP server gives an MCP-compatible AI client access to a GeoServer installation through the GeoServer REST API. It is intended for environments where an agent needs to inspect or modify published geospatial resources, perform spatial queries, or work with map and OGC service configuration.

The documented feature set covers workspace, datastore, coveragestore, layer, layer group, user, user group, feature type, attribute, and style management. It also includes system and service operations, style XML utilities, map visualization, and access to WMS and WFS services.

How it works

The primary deployment is a Python MCP process running separately from GeoServer. The process communicates with the MCP client and sends requests to the configured GeoServer REST API. A running GeoServer instance with REST enabled is therefore required, as is Python 3.10 or newer when using the Python installation.

The repository also describes a separate GeoServer extension. That artifact is installed inside GeoServer and exposes a remote MCP endpoint at /geoserver/mcp; it does not require a Python sidecar and is aimed at GeoServer 2.28.x. The extension and Python package are separate deployment options, not interchangeable installation steps.

Setup and configuration

For the Python package, install geoserver-mcp into a Python 3.10+ environment, then start the geoserver-mcp executable. The server accepts the GeoServer connection through GEOSERVER_URL, GEOSERVER_USER, and GEOSERVER_PASSWORD, or through corresponding command-line options such as --url, --user, and --password. A debug option is also documented.

Docker is another documented route. The image is mahdin75/geoserver-mcp; client configurations pass the GeoServer URL and credentials into the container as environment variables. Claude Desktop and Cursor configuration examples are included. Smithery installation is also documented for Claude Desktop, but it is an installer route rather than the project’s own server command.

Tools and capabilities

The README groups the available operations into these areas:

  • List and inspect resource endpoints
  • Manage workspaces
  • Manage datastores and coveragestores
  • Create or modify layers and layer groups
  • Manage users and user groups
  • Work with feature types and attributes
  • Manage styles and use style XML utilities
  • Perform system and service operations
  • Run spatial queries on vector data
  • Retrieve layer information and generate map visualizations

The exact tool names and request schemas are not provided in the supplied material, so client integrations should verify those details from the project documentation before building automation around them.

Limitations and notes

The project labels version 0.5.0 as beta and under active development. It requires access to a live GeoServer REST API; the MCP package is not described as a standalone geospatial database or file-processing server. Credentials must be supplied for the configured GeoServer connection, and the example values in the README should be replaced with credentials appropriate to the target installation.

The Python server and GeoServer extension should be evaluated separately. The extension’s stated compatibility target is GeoServer 2.28.x, while the supplied material does not specify an equivalent version range for the Python deployment.

Read the full README →View source on GitHub →

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Adoption & maintenance

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

GitHub stars
89
Stargazers on the source repository.
npm downloads
30k
Package downloads in the last 30 days.
Last commit
27d ago
Most recent push to the default branch.
Directory activity
4 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Geoserver MCP

Install the Python package `geoserver-mcp` in a Python 3.10+ environment, run the documented Docker image, or use the Smithery installation route for Claude Desktop.

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Technical Specs & Signals

Category🗺️Location Services
PricingFree
More technical detailsExpand ▾
TransportSTDIO
RuntimeNode.js
AuthOther
LicenseMIT
ClientsClaude Desktop, Cursor
Last updatedSep 4, 2026
11/11 checks healthy over the last 33d
Views4
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars89
GitHub Star CountTotal stargazers on GitHub representing community popularity (89 stars).
Last commit27d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 16, 2026
npm downloads30,651/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
60Quality signal: Good · 60/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools23/30
Adoption & activity11/15
Community engagement1/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 17d ago via OSV.dev · @smithery/cli (npm)

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