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

Annotations already mark the operation readOnly and idempotent, and the description adds non-obvious behavior: claim-level extraction, English-only queries, a metering/rate-limit note after 50 results, and the envelope shape with claims/experiments/provenance. No contradiction with 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 main purpose sentence is front-loaded, followed by a lean Arg/Returns structure. Every line adds usable detail, and there is no repetition of schema or annotations.

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 six-parameter tool with no schema descriptions and one sibling, the description is complete: all parameters are explained, the return envelope is summarized, and annotations already cover safety. An agent can select and call this tool without further docs.

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?

With 0% schema coverage, the description does the work: it supplies defaults, ranges, allowed values for study_type and output_format, and query style guidance. The only weakness is that min_citations is defined as 'references cited', which could be confused with a paper's reference count rather than its citation count.

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 names a specific verb and resource: 'Search claim-level evidence extracted from full-text scientific papers.' This clearly differentiates by scope from a single-paper lookup, but it never explicitly references lookup_paper, so it is not fully sibling-distinctive.

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?

It provides clear operating guidance: short English queries, use days_back/num_results/min_citations/study_type instead of stuffing filters into query text, and output_format choices. This tells an agent how to invoke the tool correctly, but it does not state when to prefer lookup_paper over search_papers.

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