deep_search

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

Despite no annotations, the description discloses key behavioral traits: no LLM call, sub-second response, dual-path retrieval, and return of pre-ranked context. It doesn't mention side effects or authentication, but for a search tool these are less critical.

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 (3 short paragraphs) with front-loaded summary, clear structure, and no redundant information. Every sentence adds value.

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 output schema covers return values, the description adequately explains the search mechanism, usage recommendation, and parameters. It lacks mention of pagination or query constraints, but overall it's fairly complete 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?

With 0% schema description coverage, the description compensates fully by explaining both parameters: query as 'The question or search terms' and project as 'Project name or UUID (uses default if empty)'. Adds meaning beyond 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 it searches beliefs and source documents with IDF-ranked results, distinguishing itself from sibling tools like 'search' by recommending it over combining multiple separate calls. The verb 'search' combined with specific resources and ranking method provides high clarity.

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 says 'This is the recommended search tool' and advises using it instead of calling 'search + list_beliefs + get_entry separately'. This gives clear when-to-use guidance and highlights efficiency benefits like 'No LLM call, sub-second response'.

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