query_natural_language

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

No annotations are provided, so the description bears full responsibility. It mentions 'execute' which implies side effects, but does not clarify whether the tool is read-only or modifies state. No details on permissions, rate limits, or what happens to the search plan after execution.

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 a single sentence, concise and front-loaded. However, it could be slightly more detailed without losing conciseness, e.g., about output format.

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 the simplicity (1 param, no output schema, no annotations), the description is minimally sufficient. It lacks details on return structure, how the plan is executed, and what 'matching entities' looks like. More context would improve usability.

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 only parameter `query` has full schema description, and the tool description adds minimal new meaning beyond restating it. With 100% schema coverage, baseline is 3, and the description does not compensate with additional semantics.

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 decomposes a natural language query into a structured search plan and returns matching entities. It provides a specific verb and resource, distinguishing it from simpler search tools like `boolean_search` or `fuzzy_search`, though it doesn't explicitly differentiate from `semantic_search` or `hybrid_search`.

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 is provided on when to use this tool versus alternatives. The description lacks any context about preferred use cases, prerequisites, or exclusions, leaving the agent to infer when this natural language planning tool is appropriate.

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