ai_search

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

Annotations already declare safe read (readOnlyHint=true) and open-ended queries (openWorldHint=true). The description adds 'AI-powered' context, implying model invocations with potential latency or cost, but does not disclose rate limits, authentication needs, or error behavior. With annotations covering core safety, a 3 is appropriate.

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 a single sentence that efficiently conveys the tool's purpose and scope. Every word adds value without redundancy or fluff.

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 low complexity (3 params, no output schema, good annotations), the description covers the high-level purpose and sources. However, it lacks details on return structure, result format, or pagination. It is adequate but not fully complete.

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 each parameter described (query, sources, limit). The description does not add any additional meaning or constraints beyond what the schema provides. Therefore, it meets the baseline of 3.

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 performs 'semantic AI-powered search' across specified sources (KB, catalog, incidents). It distinguishes from sibling tools like 'search_catalog' and 'search_knowledge' by being AI-powered and multi-source, leaving no ambiguity about its purpose.

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 does not provide guidance on when to use this tool versus similar ones like 'search_catalog' or 'search_knowledge'. It lacks explicit context on preferred usage scenarios or exclusions, leaving the agent to infer from the name alone.

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