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

Annotations already declare readOnly/idempotent/non-destructive/openWorld=false, yet the description still adds real behavior: return ordering ('newest first'), the per-paper role, the structure of the `evidence` block, and a genuine data-coverage caveat ('counts are of the papers AgingResearch.News has read, not of all the literature'). It does not mention pagination mechanics, but the cursor param covers that.

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 the use cases and the answer content, so the important routing information comes first. However, the semicolon-chained 'evidence' enumeration is dense and hard to parse, and the intervention/gene/process qualifier could be tightened.

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?

With no output schema, the description carries the return-value burden and does so: entity plus its papers newest-first, per-paper role, and the full shape of the `evidence` object. Combined with the usage questions and the caveat on counts, an agent has everything needed to select and call it.

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%, so the baseline is 3; the description earns above it by explaining the provenance of type/key ('Takes a type and key from find_entities') and giving concrete example values ('intervention rapamycin, disease sarcopenia'). It says nothing extra about limit/cursor/min_relevance, which the schema already documents.

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 concrete verb and resource ('Get a drug, gene or topic and its papers') and specifies the exact question shapes it answers (human evidence, replication, species, trials, funders). It also differentiates itself from the sibling find_entities, which supplies the type/key inputs rather than the answer.

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?

Opens with explicit when-to-use framing ('Use this for questions about one drug, supplement, gene, process or disease, such as: ...') and closes with an explicit exclusion ('Not for personal medical advice or doses'). It also names the upstream sibling (find_entities) that produces its required arguments.

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