expert_search

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

With no annotations, the description discloses polling behavior and output structure, but it does not address permissions, destructiveness, rate limits, or error handling. The polling detail adds some transparency.

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 two sentences, front-loading the purpose and then detailing behavior and output. Every word adds value 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?

Given the complexity (polling search) and absence of an output schema, the description adequately explains the output format (list of experts with details and AI summary). It lacks details on error states or cancellation, but overall it is sufficient for a search tool.

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 input schema already provides a complete description of the 'query' parameter (including examples). The tool description adds little beyond the schema's own description, so baseline 3 holds.

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: finding experts via a natural-language query using an AI-driven recommendation engine. It distinguishes itself from sibling tools like 'user_search' or 'keyword_search' by emphasizing the AI recommendation and polling behavior.

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 usage context (natural-language queries, polling for results) but does not explicitly state when to use this tool over siblings like 'user_search' or 'keyword_search', nor does it mention when not to use it.

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