For AI agents
OneGoodArea is infrastructure for AI property workflows, so we are built to be read, queried, and cited by machines, not only by people. Every entry point below is public, documented, and stable.
01 · Entry points
Four public, documented entry points. No login, no scraping, no guessing. Point a model at any of them and it can describe OneGoodArea correctly and link back to the source.
Also published: an llms-full.txt with the complete reference in one file, a discovery manifest at /.well-known/ai-plugin.json, a sitemap, a robots policy that explicitly welcomes AI crawlers, and schema.org structured data across the site.
/llms.txt. A curated map of the site for language models: what OneGoodArea is, the products, and where the canonical docs live.
/docs/mcp. Query UK areas from inside AI tools and any MCP client. The agent calls the tool and gets structured data back.
/openapi.json. The full REST API described in a machine-readable spec, so tools can generate clients and understand every endpoint.
/methodology. How every score is built, weighted, versioned, and sourced. The reference an assistant needs to explain a number.
02 · Beyond reading
OneGoodArea ships as a Model Context Protocol server, so an agent does not only read about the data, it runs the query and acts on the answer. A sample of the tools it gets:
Score any UK postcode for a chosen intent.
Pull the underlying signals for an area, each with its source.
Compare two or more areas side by side.
Find areas that match a set of criteria.
Watch a set of areas and track changes over time.
Return the exact methodology behind a score.
03 · Public and stable
Every response carries the engine version that produced it, currently v1.3.0. An answer an assistant gives today stays reproducible months from now, so it is safe to cite.
If you are putting area intelligence inside a product or an agent, the founding pilot is the fastest way in.