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DataRespublica

get_delinquance_commune

DataRespublica — dernier taux SSMSI pour 1 000 habitants (series_publiques, repli ind_delinquance).

Input Schema

NameRequiredDescriptionDefault
commune_codeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose something valuable beyond the schema: the data comes from the series_publiques dataset with an ind_delinquance fallback, so the agent learns the metric has a sourcing hierarchy. However, it says nothing about missing communes, temporal coverage of the "dernier" value, permissions, or error behavior.

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

Conciseness3/5

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

It is a single short sentence with no filler, which is good, but it is front-loaded with a vendor name (“DataRespublica”) and parenthetical internal identifiers rather than the action and return value. Density here comes from unexplained jargon rather than information.

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

Completeness2/5

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

A one-parameter tool with no output schema and no annotations needs the description to explain what comes back (rate, year, geography level) and how gaps are handled. The fallback mention is the only substantive detail; the return shape and coverage limits remain unstated.

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

Parameters2/5

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

The input schema has 0% description coverage and the description never mentions commune_code at all. It does not clarify that this is an INSEE code, what the 4–5 character constraint implies, or whether leading zeros matter — the one parameter an agent must supply is left entirely to inference.

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

Purpose3/5

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

The description names a concrete resource — the latest SSMSI rate per 1,000 inhabitants — so an agent can infer it retrieves a delinquency indicator. But the verb is nominal/implicit and the payload is wrapped in unexplained jargon (SSMSI, series_publiques, repli ind_delinquance), and nothing distinguishes it from data-fetching siblings like get_dvf_commune or get_territory_scores.

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?

There is no when-to-use guidance, no alternative-tool routing, and no stated preconditions. The only hint is the subject matter itself, leaving the agent to guess whether this or get_territory_scores / get_commune is the right call for delinquency data.

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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