Property Prices MCP Server

The Property Price Search MCP Server turns the UK Land Registry’s public SPARQL endpoint into a ready‑to‑use, type‑safe resource for AI assistants and developers. By exposing property price data through the Model Context Protocol, it removes the need for custom query construction and data parsing. Instead of wrestling with raw SPARQL responses, an assistant can simply ask for “the latest five flat prices in SW1A 1AA” and receive a structured JSON list, allowing natural‑language interactions to drive real‑world property analytics.

At its core, the server offers a single resource——that accepts a rich set of search parameters: postcode, street, city, price bounds, property type, date ranges, pagination controls, and sorting preferences. It normalises user input (e.g., converting street names to uppercase) so that queries are robust against case sensitivity issues inherent in the Land Registry data. The server then translates these parameters into a SPARQL query, executes it against the public endpoint, and formats the results as a clean object. This abstraction lets developers focus on business logic rather than low‑level data access, while still benefiting from full type safety thanks to the TypeScript implementation.

Key capabilities include:

• Fine‑grained filtering: Narrow results by price, property type, or transaction date to match specific research needs.
• Pagination and sorting: Retrieve large result sets in manageable chunks, sorted by date or price in ascending or descending order.
• Robust error handling: Clear HTTP status codes and descriptive messages guide clients in correcting malformed requests.
• IDE‑friendly stdio transport: The MCP server can be launched from a terminal or integrated into an IDE, making it convenient for rapid prototyping and debugging.

Real‑world use cases span from real estate agents generating market reports to academic researchers studying housing trends. An AI assistant could, for example, power a conversational agent that answers “How much did houses in Manchester sell for last year?” by querying the MCP server, then summarising the data or visualising price distributions. Because the server adheres to MCP standards, it can be chained with other MCP resources—such as demographic data or geospatial services—to build composite workflows that deliver end‑to‑end insights without custom glue code.

In summary, the Property Price Search MCP Server provides a declarative, type‑safe bridge to UK property price data. It simplifies integration into AI pipelines, reduces development overhead, and unlocks a wide array of analytical possibilities for developers who need reliable access to historical property transactions.