recommend_papers

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

Annotations already disclose readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is known. The description adds the concept of positive/negative examples, but this is also reflected in the schema. No additional behavioral traits (e.g., how recommendations are computed) are disclosed.

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, front-loaded sentence with no filler words. Every word earns its place, and it clearly conveys the core functionality without unnecessary detail.

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 rich schema, annotations, and output schema, the description is adequate for an agent to select and invoke the tool. It could add a bit more context about typical use cases, but the absence of such detail is not a significant gap given the structured data available.

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?

The input schema fully describes all four parameters with 100% coverage, so the baseline is 3. The description's mention of 'positive (and optionally negative) examples' adds no meaning beyond what the schema already provides.

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 uses a specific verb ('recommend') and resource ('papers') and clearly states the mechanism ('based on positive (and optionally negative) examples'). This distinguishes it from sibling tools like search_papers, get_paper, and recommend_books.

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 use case is implied: an agent should use this tool when it has example paper IDs and wants similar papers. However, the description offers no explicit guidance on when to prefer this over alternatives, nor does it mention exclusions or prerequisites.

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