recommend_papers

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

No annotations provided, so description carries full burden. It explains it uses citation graph, specifies constraints like default limit and cap at 100. However, it does not explicitly state it is a read-only operation, though it is implied. Minor gap.

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?

Highly concise: three sentences total (purpose, usage, args), no unnecessary words. Front-loaded with key information. Every sentence earns its place.

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?

For a 3-parameter tool with no output schema, it covers purpose, usage, and all parameters well. Missing description of return format (e.g., list of paper objects) but overall adequate.

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%, but the description's Args section thoroughly explains each parameter: positive_paper_ids (required), negative_paper_ids (optional), and limit (default 10, capped at 100). This adds significant meaning beyond the schema.

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 it recommends papers similar to seed papers via the citation graph. It distinguishes itself from sibling tools by contrasting with keyword search, making its unique purpose evident.

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 advises using it as a second discovery channel after keyword search, and explains how to seed it with relevant paper IDs from search results. This provides clear when-to-use and how-to-use guidance.

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