vector_store_search

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

With no annotations, the description carries the behavioral disclosure burden. It explains the auto-matching of provider/model and the ability to override, which are valuable non-obvious behaviors. It stops short of covering auth, rate limits, or error cases, but the core behavior is transparent.

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 front-loaded with a clear purpose, includes a brief default-behavior note, and then provides a compact Arg list. Every sentence contributes value, with no redundancy or filler.

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 that an output schema exists, the description sufficiently covers the tool's behavior and all parameters. It provides enough context for an agent to select and invoke the tool correctly, even without explicit failure-mode details.

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 Args section adds detailed meaning to all six parameters, including the relationship to vector_store_add, the natural-language nature of the query, the 1–50 limit bound, valid provider values, an example model, and the string form of dimensions. This fully compensates for the schema's lack of parameter descriptions.

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 opens with a specific verb ('Semantic search') and identifies the resource ('collection'), and it ties the behavior to vector_store_add's embedding route. This makes the tool's purpose clear and distinguishes it from generic search tools.

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 implies use for collections created with vector_store_add, which gives some context for when to use it. However, it does not explicitly mention alternatives like query_knowledge or provide exclusions, so usage guidance is largely implied rather than stated.

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