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Find neighbourhoods matching a question

find_areas
Read-only

Finds and ranks the neighbourhoods in England, Wales or Scotland that match a plain-English question about their signals: price, price change, crime, schools, amenities, transport, flood risk or deprivation, with filters and a sort order. Returns the ranked rows with the typed query it ran, so the answer can be checked and run again. Deprivation questions need a nation or a district, because deciles are relative within a nation. Takes 20 to 40 seconds while the question is planned. For one named place use get_area_signals or score_postcode; to compare named places use compare_postcodes; for places like a known one use find_peers. Each call uses one plain-English question from the key's monthly allowance. The question goes to a third-party language model (Anthropic, or another provider at onegoodarea.com/privacy) to plan the query; it never sees account details or result rows. There is no data for places outside England, Wales and Scotland, such as Belfast; do not call it for them.

Input Schema

NameRequiredDescriptionDefault
questionYesA question that ranks or filters neighbourhoods in England, Wales or Scotland by their signals. Examples: 'neighbourhoods in Manchester with a median price under £250k and prices rising', 'the ten least deprived neighbourhoods in Wales', 'areas in Leeds with the most Outstanding schools within 1.5 km'. Max 500 characters.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/openWorld/destructive annotations, it discloses latency (20 to 40 seconds), the per-call cost against a monthly allowance, and that the question is sent to a third-party language model that never sees account details or result rows, with a privacy link. That is unusually rich disclosure. It stops short of describing pagination or row limits on the ranked output, which is the remaining gap and the main reason this is not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is long but front-loaded and every sentence carries load: scope, return shape, prerequisite, latency, sibling routing, cost, privacy and the out-of-scope exclusion. No filler or repeated information, and the most decision-relevant facts come first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by explaining the return: 'the ranked rows with the typed query it ran, so the answer can be checked and run again.' Combined with latency, cost and scope limits, nothing an agent needs in order to decide whether to call it and what it will get back is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the schema already supplies examples and a 500-character limit, so the baseline would be 3. The description adds a real constraint the schema does not carry: deprivation questions must specify a nation or a district because deciles are relative within a nation. That is genuine added meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Finds and ranks the neighbourhoods') plus the exact geographic scope (England, Wales or Scotland) and the signals it ranks on. It explicitly distinguishes itself from the siblings find_peers, compare_postcodes, get_area_signals and score_postcode, so an agent can route correctly without opening another schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit routing rules: 'For one named place use get_area_signals or score_postcode; to compare named places use compare_postcodes; for places like a known one use find_peers.' It also names exclusions — no data for places outside England, Wales and Scotland, 'do not call it for them' (e.g. Belfast) — and a precondition that deprivation questions need a nation or district. When-to-use, when-not-to-use and alternatives are all present.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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