analyze_relevance

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

No annotations are provided, so the description carries the full burden. It discloses that the input must be from search_papers and returns sorted results with a summary, but lacks details on side effects, error handling, or internal behavior (e.g., AI usage). The output schema exists but is not described.

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 concise and front-loaded: the first sentence states the purpose, followed by typical usage and parameter details. No extraneous 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?

Given the tool has two parameters and an output schema exists, the description adequately covers the workflow and parameter expectations. It does not detail return structure (handled by output schema) or error conditions, but is reasonably complete for the complexity.

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 schema has 0% description coverage, but the description adds significant meaning: topic should be 1-3 English sentences with an example, and papers_json must be the JSON from search_papers containing a papers array. This compensates well for the bare 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 the tool analyzes relevance of papers to a research topic and returns sorted results with a summary. It distinguishes itself from siblings like search_papers (retrieval) and get_paper_detail (individual paper) by specifying the workflow and output.

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 an explicit usage guideline: first call search_papers, then pass its JSON to this method. It clearly indicates the typical use case but does not explicitly mention when not to use it or alternative sibling tools.

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