query_vectors

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

The description explains the result modes (recall, hot, cold, ephemeral) and their implications, as well as the distance metric and filter semantics. However, it omits details on pagination, rate limits, error handling, and performance characteristics.

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 well-structured, starting with the main purpose, then explaining distance, result modes, and filter. It is somewhat lengthy but each section adds value. Could be slightly more concise.

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?

For a vector search tool with no output schema, the description covers return values and result interpretations. However, it lacks details on how to specify the query vector and dataset, which are critical for invocation.

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 schema has no properties, and the description adds information about an optional flat metadata filter with exact-match AND semantics. While this provides context, it does not specify the parameter name or format, and the schema coverage is 100% by default.

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 runs a vector similarity search against a dataset and returns nearest neighbours sorted by L2 distance. This distinguishes it from sibling tools like list_vectors or get_vector, but does not explicitly specify how the dataset is identified.

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?

No guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or contrast with other search or list tools.

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