get_recommended_papers

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

No annotations; description mentions 'content similarity' but lacks details on side effects, auth, or output format. Adequate but not thorough.

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?

Front-loaded with purpose, then context, then parameters. No wasted words; efficiently structured.

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 output schema exists, description covers purpose, usage guidance, and parameter semantics. Lacks mention of limitations like data freshness, but overall sufficient.

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?

Adds concrete meaning beyond schema: paper_id accepts arXiv ID, DOI, or S2 ID; k described as recommendation count with default. Compensates for 0% schema coverage.

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?

Describes specific verb 'returns' and resource 'recommended papers based on content similarity'. Clearly distinguishes from citation-based sibling tools.

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?

Explicitly states when to use (new topic exploration, discovering related fields) and when not (citation-centered workflow). Implies alternatives like citation tools.

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