get_author_metrics

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what metrics are returned but doesn't mention critical behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or data freshness. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 appropriately concise with two sentences. The first sentence clearly states the purpose and key metrics, while the second provides usage context. There's no unnecessary repetition or fluff, though it could be slightly more structured by explicitly separating purpose from guidelines.

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 that there are no annotations and no output schema, the description is moderately complete for a simple query tool. It explains what metrics are retrieved and provides usage context, but it doesn't describe the return format, error handling, or other behavioral details that would be important for an AI agent to use it correctly. The lack of output schema means the description should ideally cover return values, which it doesn't.

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 100%, so the schema already documents both parameters ('author' and 'years') with descriptions. The description doesn't add any parameter-specific information beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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's purpose: 'Get comprehensive metrics for an author including h-index, total citations, paper count, and citation statistics.' It specifies the verb ('Get') and resource ('metrics for an author') with concrete examples of metrics. However, it doesn't explicitly differentiate from sibling tools like 'get_author_papers' or 'get_paper_metrics', which is why it doesn't achieve a perfect score.

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 provides implied usage guidance: 'Useful for CV preparation and tracking research impact.' This gives context about when to use the tool but doesn't explicitly state when not to use it or mention alternatives like 'get_author_papers' for different data. It lacks clear exclusions or comparisons to sibling tools.

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