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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds important behavioral traits: it returns only a histogram, not individual classifications, and it counts uncertain items via the confidence threshold. This complements rather than repeats the annotations.

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 and front-loaded, stating the core behavior in the first sentence and then giving concrete examples. Every sentence earns its place by defining purpose, usage, or a sibling distinction.

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 rich annotations, full parameter schema coverage, and available output schema, the description supplies everything an agent needs to select and call the tool correctly: scope, scale, result shape, and sibling distinction.

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 100%, so the parameters are already well documented and the baseline is 3. The description adds no new parameter-level meaning; it only alludes to the output-level concept of uncertainty.

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 and resource: classify up to 1,000 texts and return a histogram of label counts. It also distinguishes itself from classify_texts by emphasizing aggregate counts rather than per-item answers.

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 states when to use this tool ('when you want the shape of a corpus') and when not to ('Use classify_texts when you need the answer per item'). This gives the agent a clear routing rule versus the closest sibling.

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