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

Beyond the read-only annotation, the description reveals that the tool is AI-powered and decomposes natural language into structured filters, returns specific profile fields (affiliations, domains, publication count, top tags, recent papers), uses arXiv data from the last 12 months, and caps results at 50. This adds substantial behavioral context without contradicting the annotations.

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 well-structured: it opens with the purpose, states usage conditions, gives exclusions, explains behavior, provides output details, and includes examples. Every sentence delivers useful information with no redundancy.

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?

The description is complete given the absence of an output schema. It explains what the tool returns (profiles with affiliations, domains, publication count, top tags, recent papers), data source and time window, result limit, and example queries. It provides sufficient context for an agent to decide when and how 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?

Schema coverage is 100% with descriptions for all three parameters, so the baseline is 3. The description adds value by providing concrete examples ('quantum error correction researchers at Google', 'trapped ions', 'John Preskill') and explaining how the AI decomposes natural language into structured filters, which clarifies query semantics 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 the tool finds quantum computing researchers and potential collaborators from 1000+ active profiles, using a specific verb and resource. It explicitly distinguishes itself from sibling tools searchJobs and searchPapers, making its purpose unmistakable.

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?

It provides explicit when-to-use guidance: 'Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators.' It also gives clear exclusions and alternatives: 'NOT for jobs (use searchJobs) or papers (use searchPapers).' This fully satisfies the when/not/alternatives criteria.

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