pinecone_query_vectors

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

With no annotations, the description carries the full burden and does disclose real behavior: server-side hybrid scoring, and that a `query` string is embedded inside this call before searching. However it is silent on many behavioral traits implied by 16 params — embedding provider/model requirements, filter semantics, top_k default, and the include_metadata/exclude_expired toggles — all left for the agent to infer.

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?

Three front-loaded sentences; the core distinction against `pinecone_search` comes first and the input-mode shortcuts last. Some jargon ("client-side fusion", "true single-request") adds density without much payoff, but nothing is padded.

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?

An output schema exists, so return values need not be described. For a 16-param retrieval tool with zero schema coverage and no annotations, the description nails the input-mode logic but leaves the bulk of parameters and the embedding-requirement side-effects undocumented — adequate but with clear gaps.

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 description coverage is 0%, so the description must compensate, and it does explain the critical mutually-exclusive input modes (vector vs query vs id) plus the dense+sparse pairing. But it covers only a handful of the 16 params; filter, top_k, embed/sparse model/provider, embed_dimension, include_values, include_metadata, and exclude_expired get no semantic guidance anywhere.

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 names a specific verb and resource (query vectors) and sharpens it with the differentiator that matters: dense+sparse hybrid scoring done server-side in one request. It explicitly contrasts with the sibling `pinecone_search`, so an agent can route between them without opening either schema.

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?

It states the selecting condition clearly: use this on a single-vector index holding both a dense and sparse vector per record, because Pinecone fuses server-side rather than the client-side fusion `pinecone_search` uses. It also gives the three input modes (vector, query, id). No exclusions against the other search siblings (`pinecone_search_records`), so a 4 rather than 5.

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