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Search FR companies within a radius of a geographic point

search_companies_near_point
Read-onlyIdempotent

Find French companies whose registered office lies within a given radius of a geographic point. Enter jurisdiction 'FR', latitude, longitude, and radius in km. Results include company name, SIREN, address, and NAF code.

Instructions

Search the French registry for companies whose siège social (registered office) lies within a given radius (km) of a latitude/longitude point. Maps to recherche-entreprises' /near_point endpoint. Useful for 'companies within 2km of the Eiffel Tower' style queries. Pricing: free. Returns the same UnifiedSearchCandidate shape as search_companies. Other jurisdictions return 501 — only FR exposes this endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jurisdictionYesMust be 'FR' — only FR supports geographic search.
latYesLatitude in decimal degrees, e.g. 48.8566 (Paris).
longYesLongitude in decimal degrees, e.g. 2.3522 (Paris).
radiusNoSearch radius in km, max 50.
limitNo
activite_principaleNoOptional NAF code filter.
section_activite_principaleNoOptional single-letter NAF section A-U.
freshNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queried_atYesISO-8601 + Europe/London timezone stamp for when the registry was queried.
jurisdictionNo
latNo
lngNo
radius_mNo
countNo
resultsNo
candidatesNo
Behavior4/5

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

Annotations already declare readOnly and idempotent hints. Description adds pricing, endpoint mapping, return shape, and jurisdiction limitation. No contradictions.

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 short sentences, each adding distinct value: purpose, endpoint, example, pricing/limitation. No fluff.

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?

Covers purpose, usage constraints (jurisdiction), pricing, return shape. Output schema exists so return values are fully documented. No gaps.

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

Parameters3/5

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

Schema coverage is 75% - schema itself documents most parameters well. Description adds value by explaining the 'fresh' parameter? (not described, though fresh is not described in snippet). Actually description does not add extra parameter meaning beyond schema, but schema already does well. Baseline 3.

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?

Uses specific verb 'search' and resource 'companies near point', clearly distinguishes from sibling 'search_companies' by specifying geographic proximity and French registry. Example query clarifies use case.

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?

Explicitly states when to use ('companies within radius of point'), notes jurisdiction restriction (only FR, returns 501 elsewhere), and mentions mapping to external API. Differentiates from sibling 'search_companies' via shape and endpoint, providing clear context.

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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