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

The description reveals behavior beyond annotations: it handles aliases and typos, returns item counts and confidence, returns errors for unclear requests instead of unrelated fallsbacks, and forbids inventing slugs. The read-only and idempotent annotations are consistent with this behavior; no contradiction.

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 compact, front-loaded with the tool's purpose, and each subsequent sentence adds practical guidance: how to call it, what it returns, error behavior, and the next step. No filler or redundancy that weakens the definition.

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?

With only one parameter and no output schema, the description adequately covers return fields, error behavior, and follow-up action. It tells the agent exactly what to do after resolution, making the tool self-sufficient in context.

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 already fully documents the query parameter, including examples and the instruction to use the user's request rather than a guessed slug. The description reinforces this but does not add meaningful new parameter-level guidance beyond the 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 clearly states the tool resolves a natural-language ranking request into verified Lunoo niche slugs. It uses a specific verb ('resolve'), names the resource ('niche slugs'), and explains its role as a lookup step before get_rankings, which distinguishes it from the siblings.

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 explicitly says to pass the user's request instead of guessing a slug, and tells the agent to call get_rankings with the returned slug. It does not explicitly contrast with get_item, but the workflow guidance is clear enough for correct usage.

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