recommend_semantic_scholar_papers

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

The description reveals that the tool uses positive examples and optional negative examples to generate recommendations, which adds context beyond the tool name. However, it does not disclose any other behavioral traits such as rate limits, authentication requirements, or how negative examples influence the output. Since no annotations are provided, the description carries the full burden but only partially fulfills it.

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 concise sentence that gets straight to the point. It uses no unnecessary words and is well-structured, making it easy to parse quickly.

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?

Although an output schema exists to describe return values, the description is too sparse for a tool with four parameters and a specific recommendation mechanism. It does not clarify the role of 'limit' and 'fields', nor does it provide any usage context. An agent would need to infer parameter meanings solely from names, which may be insufficient for correct invocation.

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 description mentions positive and negative examples, which loosely map to positive_paper_ids and negative_paper_ids, but it does not explain the 'limit' or 'fields' parameters. Schema description coverage is 0%, and the description adds minimal meaning beyond what the parameter names already convey. It fails to compensate for the missing descriptions of the optional parameters.

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's function: to recommend papers based on positive examples and optionally negative examples. It uses a specific verb 'recommend' and specifies the input resource, distinguishing it from sibling tools like search or get details, and even from 'recommend_semantic_scholar_papers_for_paper' which implies a single paper input.

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 provides no guidance on when to use this tool versus alternatives such as search or the other recommendation tool. It does not mention typical use cases, prerequisites, or situations where this tool would be preferred over others. No exclusions or alternative recommendations are given.

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