recommend_papers

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

No annotations are provided, so the description carries the full burden for behavior. It adds a useful limit range (1-500, default 10), but it does not disclose how recommendations are computed, expected ordering, possible rate limits, or failure behavior. The description is not misleading, but it is thin on behavior beyond the basic request-response contract.

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 compact and front-loaded with the main purpose, followed by concise parameter explanations. The Args block partially duplicates the input schema, but it remains clear and useful without unnecessary fluff.

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?

The tool is relatively simple and has an output schema, so return-value details are not required. However, the description does not cover when to choose recommendations over the related citation/reference tools or how the recommendation source is defined, leaving some selection and expectation gaps for an agent.

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 compensates by explaining both parameters: paper_id is a Semantic Scholar paper ID and limit is the maximum recommendations count with range and default. This adds meaning beyond the schema's type and title fields.

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 verb and resource: 'Get paper recommendations based on a given paper.' This distinguishes it from search_papers, but it does not explicitly contrast with get_citations or get_references, so sibling differentiation is only implicit.

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 description does not say when to use this tool versus alternatives such as get_citations or get_references. Usage context is only implied by the tool name and the phrase 'based on a given paper.' There is no explicit when-to-use guidance or exclusion criteria.

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