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autocomplete_commune

Read-onlyIdempotent

Recherche de communes françaises par nom, code postal ou code INSEE. Idéal pour autocomplétion. Source : geo.api.gouv.fr (DINUM/Etalab).

Un (au moins) parmi nom, codePostal, code est requis. Alias acceptés : q/query/searchnom, codepostal/postal_codecodePostal, code_insee/inseecode.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nomNoRecherche par nom (autocomplétion). Ex: "Villeneuve d'Ascq", "Lyon".
codeNoCode INSEE exact (5 caractères). Ex: "59009".
limitNoNombre max de résultats (1-30, défaut 10).
codePostalNoCode postal exact (5 chiffres). Ex: "59650".
boostPopulationNoTrier par population décroissante. Recommandé pour les noms ambigus (ex: 'Charleville').

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by stating that at least one of nom, codePostal, or code is required, which is a key behavioral constraint not fully captured in the input schema (no required parameters).

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?

The description is three sentences long, front-loaded with purpose and source, then constraints, then aliases. Every sentence adds value with no redundancy or fluff.

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

Completeness3/5

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

The description covers purpose, constraints, and aliases well, but with no output schema, it could mention what the tool returns (e.g., a list of communes with basic fields). This omission slightly reduces completeness for a tool with 5 parameters.

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%, so baseline is 3. The description adds semantic value beyond the schema by providing aliases (e.g., q/query → nom) and explicitly stating the requirement for at least one parameter. The schema also includes examples, but the description's aliases enhance discoverability.

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

Purpose4/5

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

The description clearly states the tool searches French communes by name, postal code, or INSEE code, and is ideal for autocomplete. It is specific but does not explicitly differentiate from sibling get_commune_by_code, which might be more appropriate for exact code lookup.

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

Usage Guidelines2/5

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

The description provides minimal usage guidance: it notes the tool is ideal for autocomplete and mentions the data source. However, it does not specify when to use this tool over siblings like get_commune_by_code, nor does it state when not to use it.

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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TDQS

A4/5.0
Disambiguation3/5

While tools have distinct purposes, there is overlap among several similar tools (e.g., multiple professional and establishment search tools). The detailed descriptions help differentiate, but an agent may struggle to choose correctly among them.

Naming Consistency2/5

Naming mixes French and English, with no consistent pattern (e.g., 'enrichir_concurrents' vs 'inspect_site', 'etablissement_by_finess' vs 'etablissements_finess_in_radius'). This inconsistency makes the toolset harder to navigate.

Tool Count3/5

36 tools is high but justified given the broad domain. However, there are multiple tools for similar tasks (e.g., four professional search tools), suggesting some redundancy. The scope is borderline but acceptable.

Completeness4/5

The toolset covers a wide range of needs for French health data analysis: establishments, professionals, population, geocoding, demographics, and composite analyses. Few obvious gaps exist, though some specialized tasks might require additional integration.