Geolocation MCP Server

The Geolocation MCP server solves the common developer challenge of enriching AI interactions with accurate, location‑based context. When building conversational agents that need to answer questions about walkability, public transit options, or bike friendliness, having a lightweight, plug‑in server that can translate addresses and coordinates into actionable data is essential. This MCP brings a single, well‑defined interface to the WalkScore API, turning raw geographic inputs into structured scores and transit information that an AI assistant can easily consume.

At its core, the server exposes two tools: and . The former accepts latitude and longitude and returns a list of nearby transit stops, including route identifiers and distances. The latter is more versatile; it can take an address string, coordinates, or both, and responds with three scores—WalkScore, TransitScore, and BikeScore—all ranging from 0 to 100. By aggregating these metrics into a single response, the server allows an AI assistant to provide concise, actionable insights such as “This address scores 85 for walkability and has two nearby bus stops within a quarter mile.”

Developers benefit from the server’s location‑agnostic design. Whether an application supplies a full postal address or just GPS coordinates, the MCP handles both seamlessly, offering a fallback for missing data. The integration with AI workflows is straightforward: once the MCP server is running, any compatible client—Claude.app, VS Code, or a custom chat interface—can invoke the tools via simple JSON messages. The assistant can then embed the results directly into replies, enrich user prompts with contextual data, or trigger downstream actions like route planning.

Real‑world scenarios for this MCP are plentiful. Urban planners can query neighborhoods to compare walkability across districts; real‑estate agents can present potential buyers with transit scores for listings; event organizers might assess bike accessibility to nearby venues. Because the server wraps a commercial API in an open, protocol‑based interface, teams can swap providers or upgrade to premium tiers without changing client code.

Unique advantages of the Geolocation MCP include its tight coupling with WalkScore’s comprehensive dataset and its dual‑mode input handling. By supporting both addresses and coordinates, it reduces the friction of data validation and improves accuracy for applications that already possess geospatial metadata. The server’s minimalist footprint—just two tools, a single environment variable for the API key, and no complex orchestration—makes it an ideal addition to any AI‑powered workflow that requires reliable, up‑to‑date location intelligence.