Find authors by name

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds real value beyond that by disclosing the return shape (one line each with author id, name, works count, citation count) — meaningful since there is no output schema. It doesn't mention result limits or ranking among candidates, which keeps it from a 5.

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?

Front-loaded with purpose and return format, then inputs and an example — well ordered with no fluff. The final example prompt largely restates the opening intent, a minor redundancy rather than a defect.

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?

For a one-parameter lookup with no output schema, the description covers purpose, next step, and the fields returned per result line. The remaining gap is how many candidates come back and how ties are ordered, which matters for a disambiguation tool.

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% and the single parameter is documented there with the same 'Jane Doe' example, so the description's restatement of name as required adds little. The mention of matching behavior ('candidate authors matching a name') hints at partial-match semantics but doesn't specify matching rules. Baseline 3 is appropriate when the schema carries the parameter burden.

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?

States a specific verb and resource ('candidate authors matching a name') and immediately frames the intent: pick the right one before listing their papers. That explicitly distinguishes it from the sibling list_author_papers, so an agent can route between the two without opening either schema.

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 workflow context ('so you can pick the right one before listing their papers') plus the example prompt makes the intended call sequence clear. It stops short of naming explicit exclusions or prerequisites, but the disambiguation-first framing gives strong usage signals.

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