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Hug0x0

mcp-reunion

reunion_get_jobseekers_by_commune

Retrieve jobseeker counts in La Réunion grouped by commune, with INSEE code and postal code. Combine with commune population data to calculate unemployment rates.

Instructions

Pôle emploi (France Travail) jobseeker counts in La Réunion broken down by commune of residence, per snapshot date. Returns date, commune, INSEE code, postal code, jobseeker count. Sorted by date then jobseekers descending. Combine with reunion_get_commune_population (commune module) to compute unemployment rates per commune. Source: France Travail / Pôle emploi via data.regionreunion.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
communeNoCommune name prefix match (e.g. "Saint-Denis", "Saint-Pierre")
postal_codeNoExact postal code (5 digits, Réunion uses "974xx")
limitNoMax records to return (1-500, default 50)
Behavior3/5

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

The description reveals sorting order (by date then jobseekers descending) and data source, but with no annotations, it does not explicitly state read-only behavior, rate limits, or authentication needs. It adds value but could be more comprehensive.

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-loads the core purpose and return fields, then adds sorting and a combination use case, and finishes with the source. Every sentence is informative and no filler.

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

Completeness4/5

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

For a data retrieval tool with no output schema or annotations, the description covers purpose, fields, sorting, source, and a use case. It could mention pagination (though limit is in schema) or error handling, but it is sufficient for an AI agent to decide and invoke correctly.

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?

Input schema has 100% coverage with descriptions for each parameter, so the description adds minimal extra meaning beyond sorting and source. It does not elaborate on parameter behavior or constraints beyond what schema provides.

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 clearly states the tool provides jobseeker counts per commune in La Réunion, broken down by snapshot date, and specifies the returned fields (date, commune, INSEE code, postal code, count). It distinguishes from siblings like reunion_get_jobseekers_by_age_sex and suggests combining with reunion_get_commune_population.

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

Usage Guidelines4/5

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

The description explicitly recommends combining with reunion_get_commune_population to compute unemployment rates, providing a concrete use case. It does not list when not to use or alternative tools, but the sibling context and clarity of purpose make it adequate.

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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