Ordnance Survey MCP Server

The Ordnance Survey MCP server bridges the gap between large‑language models and the rich geospatial datasets provided by the UK’s national mapping agency. By exposing Ordnance Survey Data Hub APIs through the Model Context Protocol, it allows AI assistants such as Claude to query and manipulate detailed location data without requiring the developer to write custom API wrappers or handle authentication manually. This is particularly valuable for applications that need precise, up‑to‑date information on streets, planning zones, or points of interest across the United Kingdom.

At its core, the server implements a two‑step workflow that guarantees consistent, high‑quality results. First, an LLM can pose a natural‑language request—e.g., “find all cinemas in Leeds City Centre”—which the server translates into a structured API call to the OS Data Hub. Second, the response is fed back into the model in a context‑aware format that preserves spatial relationships and metadata. This disciplined approach prevents common pitfalls such as ambiguous queries or incomplete data retrieval, ensuring that developers receive complete and accurate information ready for downstream processing.

Key capabilities include:

• Rich geospatial querying: Access to layers such as street works, planning applications, and point‑of‑interest datasets.
• Prompt templates: Predefined prompts for complex use cases (e.g., evaluating planning impact or assessing infrastructure risk).
• Resource management: Automatic handling of API keys and rate limits through environment variables.
• Tool integration: Seamless addition to Claude Desktop, exposing a suite of tools, resources, and prompts that can be invoked directly from the chat interface.

Real‑world scenarios that benefit from this server are plentiful. Urban planners can quickly assess the effect of new developments on surrounding transport networks; event organizers might locate suitable venues within a specific radius; logistics companies can map optimal delivery routes that respect local restrictions. Because the server enforces a structured interaction pattern, developers can build robust pipelines where an AI assistant gathers data, a human reviews it, and another system consumes the refined output—creating efficient, end‑to‑end workflows.

Unique advantages of the Ordnance Survey MCP server include its tight coupling with the official OS Data Hub, guaranteeing data accuracy and freshness, and its lightweight Docker deployment that requires no complex configuration. The server’s open‑source nature encourages community contributions, while the explicit two‑step workflow provides a clear contract between AI and data sources—making it an ideal foundation for any geospatial application that relies on conversational AI.