Overview

The OpenStreetMap MCP Server is a lightweight, protocol‑ready bridge that lets AI assistants retrieve and render geographic data directly from the OpenStreetMap (OSM) ecosystem. By exposing a fully compliant Map Control Protocol (MCP), it enables assistants such as Claude to query map tiles, layers, and metadata without needing to embed a heavy GIS stack within the assistant itself. This separation of concerns allows developers to keep AI models focused on natural‑language understanding while delegating spatial intelligence to a dedicated service.

The server implements the MCP specification on top of a conventional RESTful API, providing endpoints for tile retrieval, vector layer access, and OSM feature queries. It serves standard raster tiles in formats like PNG or WebP, as well as vector tiles that can be consumed by modern mapping libraries. Because the service is stateless and cache‑friendly, it scales horizontally with minimal configuration, making it suitable for both small prototypes and production deployments that handle thousands of concurrent map requests.

Key capabilities include:

• OSM data integration: Direct access to the full OpenStreetMap dataset, including recent edits and rich attribute information.
• MCP protocol compliance: Seamless integration with any MCP‑aware AI client, allowing declarative map control commands (e.g., pan, zoom, layer toggling) to be translated into HTTP requests.
• RESTful API: Human‑readable endpoints for debugging, monitoring, and custom tooling.
• Tile serving: Efficient delivery of map tiles with support for common caching headers and optional compression.

Typical use cases involve conversational agents that need to answer location‑based questions, provide directions, or visualize points of interest. For example, a travel assistant could ask the server to render a map centered on a user’s destination and overlay recommended restaurants. Developers can embed these map views directly into chat interfaces or web dashboards, giving users an interactive spatial context without leaving the AI conversation.

Integrating the server into an AI workflow is straightforward: the assistant issues MCP commands (e.g., “display map at zoom 14, center on coordinates X,Y”), which the MCP client translates into HTTP requests to the server. The response—either a tile image or vector data—is then rendered in the client UI. Because the server is agnostic to the AI model, it can be paired with any assistant that supports MCP, providing a modular and future‑proof solution for spatial intelligence in AI applications.