get_publication_stats

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

Annotations already declare readOnlyHint=true, so the description need not state it is read-only. The description adds that it returns counts per facet and lists valid group_by values, which is useful context. It does not describe output structure or potential limits, but given the annotation coverage, a 3 is appropriate.

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 concise and well-structured: a one-sentence summary, a use-case list, and a clear Args section. No superfluous content; every sentence earns its place.

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?

The tool is simple with only two parameters, and an output schema exists, so the description need not detail return values. It covers purpose, parameters, and usage context adequately. A minor gap is the lack of any mention of result ordering or limits, but overall it is complete for the tool's scope.

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 description coverage is 0%, so the description must explain the parameters. It clearly defines query as search terms defining the set to analyze and group_by with allowed values ('annee', 'type', 'motscles', 'auteur'). This fully compensates for the sparse schema, providing meaning beyond the bare field names.

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 provides aggregated statistics on publications (counts by facet) and lists concrete use cases (lab production by year, document type distribution, dominant keywords, top authors on a theme). This distinguishes it from sibling tools like search_publications or get_publication, which focus on individual records or searches.

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 includes a 'Utile pour' section that gives explicit use cases, making it clear when to call this tool. However, it does not explicitly state when not to use it or name alternative tools for other scenarios. It provides context but lacks exclusion criteria.

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