query_relevant

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

With no annotations, the description carries the full burden, and it fulfills it by detailing the underlying system behavior (hybrid retrieval, probability ordering) and explaining what the boolean flag does ('also return, per belief, the document passages that ground it').

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 extremely dense and concise. It immediately front-loads the core mechanism and efficiently explains the functionality of the most important parameters in subsequent sentences.

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?

The description is highly complete regarding the complex logic (hybrid search, evidence handling, project scoping). It only misses mentioning parameters like `limit` or `prefer_type`, which are details better suited for the schema itself.

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?

Given the low schema coverage (33%), the description compensates significantly by adding crucial semantics. It explains that `include_evidence` provides 'document passages that ground it (with weights),' which is much richer than a simple boolean 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 goes beyond a simple 'search' by specifying the exact mechanism: 'Hybrid retrieval: text match + graph-proximity expansion,' which immediately tells the agent how the information is gathered and ranked by probability.

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 provides excellent internal usage guidelines by detailing the conditional effects of key parameters. It tells the agent to use `include_evidence=true` for grounding context and specifies that `project` restricts the search space to a specific pool.

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