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Decode a registry code

explain_code

Translate French registry codes into plain French. Decode NAF activity codes, legal forms, workforce brackets, and administrative statuses without an API call.

Instructions

Translate a NAF activity code, catégorie juridique (legal form), workforce bracket, or administrative status into plain French. Offline — no API call. Use this instead of guessing what a code means.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYese.g. '62.01Z', '5710', '12', 'A', 'PME'
Behavior3/5

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

With no annotations provided, the description must carry behavioral disclosure. It adds useful traits: the operation is offline ('no API call') and translates several code categories into plain French. It stops short of describing error behavior, output format, or what happens for unsupported codes, so it is adequate but not rich.

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?

Three short sentences with no redundancy. The primary action and scope are front-loaded, followed by an important execution detail (offline) and a useful usage cue. Every sentence earns its place.

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?

This is a minimal single-parameter tool with no output schema. The description covers the code categories, the offline behavior, and the high-level output ('plain French'), which is enough for an agent to decide and invoke correctly. Minor gaps like invalid-code handling do not significantly reduce completeness.

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% and the parameter already includes examples ('62.01Z', '5710', '12', 'A', 'PME'). The description adds semantic meaning by classifying these examples into NAF, legal form, workforce bracket, and administrative status, helping the agent understand what value to pass.

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 states a specific verb ('Translate') and a clearly bounded resource: NAF activity codes, legal forms, workforce brackets, and administrative statuses. This is immediately distinguishable from the sibling tools (search_companies, get_company, list_establishments), which are about company/establishment data rather than code decoding.

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?

It gives clear context for when to use the tool ('Use this instead of guessing what a code means') and adds the offline constraint. However, it does not explicitly name alternatives or state when not to use it, though the sibling tools are sufficiently different that the guidance is 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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