expert_search

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

With no annotations, the description carries the full burden. It discloses that the tool polls until search completion, the meaning of results_pending, the fields returned, and the need to use get_expert_search for pending results. This is substantial behavioral context, though it does not cover error conditions or all possible statuses.

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 at two sentences, with the core purpose front-loaded. Every sentence adds value: the first states the primary function, the second explains async behavior and output format. No wasted words.

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's simplicity (one parameter) and lack of output schema, the description provides a complete picture of what to expect: the search behavior, the result list fields, and the results_pending edge case. It does not describe the initial poll response structure, but the overall coverage is strong.

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 100% (the 'query' parameter is well-described as a natural-language description with examples). The tool description adds little beyond repeating that the query is natural-language, so it does not exceed the baseline for high 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?

The description clearly states the tool's purpose: 'Find experts matching a natural-language query using Rolli's AI-driven recommendation engine.' This is a specific verb (find) and resource (experts) that distinguishes it from siblings like keyword_search and user_search. The scope is unambiguous.

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 implies when to use this tool: for natural-language expert searches. It also provides explicit follow-up guidance: 'A status of results_pending means the search succeeded but results are still being written; fetch them with get_expert_search.' However, it does not explicitly contrast with keyword_search or other search variants, leaving some room for ambiguity.

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