OpenStreetMap MCP Server

The OpenStreetMap MCP Server equips large‑language models with a rich set of geospatial capabilities that go beyond generic knowledge. By exposing OpenStreetMap data through the Model Context Protocol, it allows AI assistants to transform natural‑language location queries into actionable geographic information. This means a conversational agent can now understand where a user is, suggest nearby amenities, or plan routes—all within the same dialogue flow.

At its core, the server provides a suite of tools that mirror common mapping functions: geocoding, reverse geocoding, nearby‑place discovery, turn‑by‑turn routing, and category searches within a bounding box. These primitives are wrapped in MCP tool definitions, enabling LLMs to call them directly from prompts. The server also offers higher‑level utilities such as optimal meeting point calculation for groups, neighborhood livability analysis, and EV charging station filtering. Such advanced features turn a simple map query into a comprehensive travel or real‑estate recommendation, which is especially valuable for developers building itinerary planners, property search assistants, or smart city dashboards.

The server’s resources expose raw map tiles and place details through URL patterns ( and ). This dual approach—tools for dynamic interaction and resources for static content—provides flexibility: an assistant can fetch a styled map image on demand or retrieve structured place data for downstream processing. The design aligns with MCP’s intent to keep the client and server loosely coupled, allowing any host that supports MCP (Claude Desktop, Cursor, Windsurf) to tap into OpenStreetMap without bespoke integrations.

Real‑world scenarios include a travel chatbot that recommends restaurants, public transport routes, and parking near an event venue; a real‑estate assistant evaluating neighborhoods for potential buyers; or an EV driver’s companion that locates nearby charging stations with specific connector types. In each case, the MCP server acts as a bridge between conversational intent and precise geospatial data, dramatically reducing the development effort required to add location intelligence to AI products.

What sets this implementation apart is its comprehensive coverage of everyday mapping needs coupled with a developer‑friendly API surface. The server’s tools are intentionally high‑level yet configurable, allowing fine‑grained filtering (e.g., parking fee thresholds or EV connector types). Moreover, the inclusion of demo GIFs in the README showcases practical use cases right out of the box, giving developers a clear picture of how to weave geospatial reasoning into their AI workflows.