nlm_trend_research

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

No annotations are provided, so the description carries the full burden. It mentions a multi-step process (fetch, research, create) but does not elaborate on side effects, authentication needs, rate limits, or what 'grounded drafts' imply. The description is too brief to be transparent.

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 a single sentence of 10 words, which is concise. However, it lacks structure and front-loads the actions without separating the steps clearly. It could be improved by listing the phases.

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 the three parameters (including an enum and defaults) and the availability of an output schema, the description is insufficient. It does not explain the returned data, parameter choices, or the expected workflow. The output schema likely covers the return, but description should still summarize key points.

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 0%, and the description does not mention any of the three parameters (geo, count, platform). The description fails to add meaning beyond the schema, leaving agents without guidance on parameter usage.

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 fetches trend-pulse topics, researches/imports them, and creates grounded drafts. The verb 'Fetch' and action 'research/import' and 'create' specify what the tool does, but 'trend-pulse topics' is vague. It implies trending content, distinguishing it from general research tools like nlm_research.

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?

No guidance on when to use this tool versus alternatives like nlm_research or nlm_research_pipeline. The description does not mention prerequisites, when not to use, or specific scenarios.

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