semantic_scholar_recommendations

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

Annotations already indicate readOnlyHint, idempotentHint, openWorldHint. The description adds no behavioral details beyond that, such as side effects, rate limits, or that recommendations are based on similarity metrics.

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 front-loads the core purpose. Every word is necessary; no 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?

Given the existence of an output schema and annotations, the description is minimally complete. However, it lacks explanation of the recommendation rationale or use cases, which could help an agent decide when to invoke it.

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?

Although the schema itself contains descriptions for parameters, the context indicates schema description coverage is 0%, meaning the tool description should compensate. The brief description does not add information about parameters beyond what schema provides, so it's adequate but not enhanced.

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 gets paper recommendations based on a seed paper. It specifies the verb 'Get' and resource 'paper recommendations', distinguishing it from siblings like search or get single paper. However, it does not differentiate from the likely similar tool 'semantic_scholar_multi_recommend'.

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 vs. alternatives like search or other recommendation tools. No exclusions, prerequisites, or context are given.

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