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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered structurally. The description adds one genuinely useful behavioral fact — that results come from a real algorithm engine rather than LLM generation — but says nothing about result limits, ordering, or how max_results interacts with the returned list.

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 short and front-loads the topic, but it is written as fragmentary label text rather than sentences, and the bracketed "【PubMed 检索】" largely duplicates the annotation title "PubMed 检索". One parenthetical, "真实算法引擎执行，非大模型生成", is the only non-redundant value-carrying content.

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?

No output schema exists, yet the description only says a list of PubMed records is returned without indicating record fields, result caps, or whether year_start alone is accepted. For a three-parameter search tool this is minimally adequate, but it leaves gaps an agent would need to probe at call time.

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%, with q, year_start and max_results each documented in the schema, so the baseline of 3 applies. The description adds no parameter meaning beyond the schema — notably it does not clarify that q must be an English query string, which is the schema's most consequential constraint.

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?

"PubMed 检索" names a specific resource and action, and the description also states the return value ("PubMed 文献列表"), so an agent knows this queries the PubMed corpus. It does not differentiate from related siblings such as act_lit_search or act_lit_verify_citations, which is the only reason it falls short of a 5.

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?

"用户要查 PubMed、要最新文献" gives an implied trigger condition, but it is not a real when-to-use rule: there is no statement of when NOT to use this tool and no routing to act_lit_search or act_lit_verify_citations for non-PubMed or verification needs. Usage is inferable but not delineated.

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