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UK Crimes Near Coordinate

gov.ukpolice.crimes_near
Read-onlyIdempotent

Retrieve street-level UK crime records within 1 mile of a coordinate for a specific month. Filter by category such as burglary or violent crime. Get up to 500 records with location and outcome status.

Instructions

Street-level UK crime records within 1 mile of a coordinate for a given month (England + Wales, 43 forces). Filter by crime category. Returns up to 500 records with category, location, outcome status. OGL v3.0 (commercial OK)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude (e.g. 51.5074 for central London). UK area only.
lngYesLongitude (e.g. -0.1278 for central London).
dateNoMonth in YYYY-MM format (e.g. "2024-06"). Defaults to latest available month if omitted.
categoryNoCrime category slug — "all-crime" (default), "burglary", "violent-crime", "drugs", "robbery", "shoplifting", "vehicle-crime", "anti-social-behaviour", etc.
limitNoMax crime records to return (default 100, max 500).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
Behavior4/5

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

Annotations already indicate read-only, idempotent, safe operation. Description adds useful behavioral context: data source (UK police API), license (OGL v3.0, commercial OK), maximum records (500), and returned fields (category, location, outcome status). No contradictions with annotations.

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?

Four terse sentences, each earning its place: main function, filtering, return details, license. Front-loaded with core purpose. No superfluous words.

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?

Given the presence of output schema and 100% schema coverage, description covers all needed context: geographic scope, data source, license, return limits, filtering capability. No missing information for correct invocation.

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 descriptions cover each parameter with defaults and examples. Description adds the critical radius constraint ('within 1 mile') not present in schema. It also contextualizes the date default. Meaningful addition beyond 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?

Description clearly states it retrieves street-level UK crime records near a coordinate for a given month, specifying geographic scope (England+Wales, 43 forces). This distinguishes it from sibling tools like gov.ukpolice.forces (list forces) and gov.ukpolice.outcomes (crime outcomes).

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

Usage Guidelines3/5

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

Usage is implied from the description (for querying crime data near a location), but no explicit guidance on when to use vs. alternative tools (e.g., forces, outcomes) or when not to use. Could be improved by mentioning specific use cases or constraints.

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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