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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds transparency by listing the exact data points returned (date, ingestion run, counts, age), which is useful beyond the annotations. There is no contradiction between description and annotations.

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

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

The description is two sentences long, front-loaded with the most important information, and includes a concrete example. Every sentence adds value: the first lists what data is returned, the second tells when to use it. No wasted words.

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

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

Given the tool has no parameters and no output schema, the description fully explains what the tool does and what it returns. It is sufficient for an AI agent to understand the purpose and output. The example further clarifies the expected query format.

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

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

The tool has zero parameters, so the input schema is trivially 100% covered. The description does not need to explain parameters. The description adds value by specifying the kind of information returned, which is effectively the 'output' semantics. Baseline for 0 parameters is 4.

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

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

The description clearly states the tool returns 'source dataset date, ingestion run, counts (total/active/RNCP/RS/blocs/partners) and age of the data in days.' It also provides a concrete usage example ('De quand datent les données RNCP ?'), making the purpose unambiguous. The tool's name 'get_data_status' is accurately reflected, and it is distinct from sibling tools like 'get_certification' or 'list_blocs' which focus on specific entities.

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

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

The description explicitly says 'Call this to tell the user how fresh the answers are,' which is a clear usage guideline. It includes a realistic example question in French. While it does not provide 'when not to use' or contrast with siblings, the use case is specific enough that an AI agent can infer when to invoke it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.