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

Annotations show readOnlyHint, idempotentHint, and non-destructive. The description adds crucial behavioral details: Lisibilite_OCR excludes non-French items counted in readability_excluded, MATTR is length-robust, and metrics exist only for public full-text items. This provides transparency beyond annotations without contradiction.

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 about 100 words, front-loading the purpose then adding nuances. Each sentence adds value (e.g., clarifying metric behavior, language handling). However, it could be slightly more structured with bullet points or clearer separation of metrics, but it's efficient and concise.

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 output schema exists and the tool has 8 parameters with no required ones, the description comprehensively explains the metrics' interpretation, language filtering, and data availability. It covers key caveats without needing to describe the return structure (handled by output schema).

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?

Schema coverage is 100%. The description does not add any parameter-specific semantics beyond what the schema provides. Each parameter is already well-described in the schema (e.g., top_n description includes default and max). Therefore, the description adds no extra value for parameters, staying at baseline 3.

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 that the tool computes readability, lexical richness, and length averaged by year, newspaper, or country. It specifies the exact metrics (Lisibilite_OCR and Richesse_Lexicale_OCR) and how they are computed. This distinguishes it from sibling tools like get_temporal_distribution which focus on trends, not lexical metrics.

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 gives explicit usage guidance: 'do not normalise it by word count or bin it by length' for MATTR. It explains when to use each metric (readability only for French text, MATTR covers everything) and notes that metrics are only available for items with full text. While it doesn't explicitly compare to siblings, it provides clear context and constraints.

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