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

Annotations (readOnlyHint, openWorldHint, destructiveHint) already cover safety and result variability. The description adds meaningful behavioral context: uses kNN vector similarity and finds semantically related content even without exact keywords.

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, with key information front-loaded in the first sentence. The two paragraphs are well-structured and efficient, though minor redundancy exists.

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 presence of an output schema and comprehensive annotations, the description adequately explains the tool's mechanism, use case, and differentiation from siblings. It could partially describe result format but is complete enough for selection.

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?

Input schema coverage is 100%, with clear descriptions for both parameters. The description reinforces that 'query' is natural language and adds context about embedding-based matching, but does not substantially augment 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 specifies the tool finds discussions 'by meaning rather than exact words' using embedding-based semantic search, clearly distinguishing it from keyword-based sibling tools like search, search_elastic, and search_mysql.

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 states it is 'best for natural language questions and conceptual searches,' providing clear context for when to use it. However, it does not explicitly mention when not to use it or name alternatives.

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