vector_query

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

Annotations already indicate read-only, non-destructive, idempotent. The description adds context about returning sorted results or errors, and specifies constraints like topk max=1000. No contradictions.

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?

Well-organized with clear sections (purpose, args, returns, examples). Front-loaded with main action. Every sentence contributes meaning without 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?

Explains main functionality and parameters well, but lacks details on the exact structure of returned search results (e.g., whether it includes scores or metadata). Adequate for a similarity search tool with good schema and annotations.

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 has descriptions for all nested properties, so baseline is 3. The description adds value by providing filter syntax examples and clarifying the purpose of each parameter in context, but it largely replicates schema info.

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 'Perform vector similarity search with optional filtering' with a specific verb and resource. It distinguishes itself from siblings like multi_vector_query by focusing on single-vector search with filters.

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?

Provides explicit use cases (e.g., 'Find the 10 most similar documents') and filter syntax examples. However, it does not mention when not to use it or compare to alternative sibling tools like multi_vector_query.

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