GeoServer MCP Server

The GeoServer MCP Server bridges the gap between large language models and geospatial services by exposing the full capabilities of a GeoServer instance through the Model Context Protocol. It turns any GeoServer REST endpoint into a rich, AI‑friendly toolkit that lets assistants query, manipulate, and visualize spatial data without writing custom code. This eliminates the friction of integrating OGC services into conversational workflows, enabling developers to focus on higher‑level logic while the MCP handles authentication, request formatting, and response parsing.

At its core, the server provides a set of intuitive tools that map directly to GeoServer concepts: workspaces, layers, styles, and services. Developers can list available workspaces, retrieve detailed layer metadata, perform spatial queries on vector features, and even generate map images via WMS. The toolset also supports CRUD operations on layers and styles, allowing assistants to create or update geospatial resources dynamically. These actions are exposed through simple JSON payloads, making them consumable by any MCP‑compatible client such as Claude Desktop or Cursor.

Key features include:

• Workspace and Layer Management – List, create, update, or delete workspaces and layers with a single command.
• Spatial Querying – Execute WFS queries to filter features by bounding boxes, attributes, or spatial predicates.
• Map Generation – Generate WMS images on demand, specifying style, scale, and output format.
• Service Discovery – Retrieve WMS/WFS capabilities documents to understand available layers and operations.
• Authentication Handling – Securely pass GeoServer credentials via environment variables, keeping secrets out of client code.

Real‑world scenarios that benefit from this server are plentiful. A field data collection app can ask an AI assistant to upload new shapefiles, automatically publish them as layers, and generate a preview map. Urban planners can query zoning information by location and receive an instantly rendered map in the conversation. Environmental analysts might request a heatmap of pollution levels across multiple datasets, with the MCP orchestrating the underlying WFS requests and map rendering.

Integrating GeoServer MCP into an AI workflow is straightforward: the assistant invokes a tool like or , receives structured JSON, and can embed the results in natural language responses. Because the server adheres to MCP’s standard payload format, any client that understands MCP can leverage these geospatial capabilities without bespoke adapters. This plug‑and‑play model accelerates prototyping, reduces boilerplate, and opens geospatial data to a wider range of AI‑driven applications.