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OSMMCP: OpenStreetMap MCP Server

MCP Server

Precision geospatial tools for LLMs via MCP

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About

OSMMCP is a Model Context Protocol server that offers geocoding, routing, nearby places, neighborhood analysis, and EV charging station lookup for OpenStreetMap data. It enables LLMs to integrate accurate geospatial capabilities into applications.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

OSMMCP: OpenStreetMap MCP Server

OSMMCP is a Model Context Protocol (MCP) server that equips large language models with robust geospatial capabilities derived from OpenStreetMap data. By exposing a suite of precision tools—geocoding, routing, nearby place discovery, neighborhood analytics, and electric‑vehicle charging station lookup—OSMMCP allows AI assistants to answer location‑centric queries with real‑world accuracy and contextual depth.

The server solves a common pain point for developers building AI‑powered mapping or travel applications: the need to query multiple external services (e.g., Google Maps, Mapbox) and stitch together disparate results. OSMMCP consolidates these functions into a single, consistent MCP endpoint set, enabling seamless integration with any LLM that supports the protocol. This reduces latency, simplifies authentication management, and ensures that data remains open‑source and free from vendor lock‑in.

Key capabilities are presented through straightforward HTTP endpoints that translate natural language prompts into structured geospatial requests. For example, a user can ask an assistant to “find the nearest EV charging station” and the MCP server will return coordinates, operator details, and availability. The neighborhood analysis tool provides demographic insights—population density, median income, and nearby amenities—making it ideal for urban planners or real‑estate agents seeking to evaluate a location’s potential. Routing delivers turn‑by‑turn directions and travel time estimates, supporting logistics, delivery optimization, or personal navigation workflows.

In practice, OSMMCP fits into AI pipelines where contextual grounding is essential. A conversational agent can use the geocoding tool to resolve ambiguous place names, then pass coordinates to the routing tool to suggest optimal travel routes. Developers can chain these tools with other MCP services—such as weather or traffic—to build comprehensive travel assistants. The EV charging endpoint is particularly valuable for sustainability‑focused applications, allowing planners to map out charging infrastructure and recommend routes that minimize downtime.

What sets OSMMCP apart is its commitment to open data and protocol‑first design. Because it relies solely on OpenStreetMap, the server can be self‑hosted without external API limits or costs. The MCP interface guarantees that any LLM—Claude, GPT‑4o, or custom models—can consume the same tool set with minimal adaptation. This uniformity empowers developers to create rich, location‑aware experiences while keeping infrastructure lean and cost‑effective.