Classify items against a shared label set

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

With no annotations, the description carries the full burden. It transparently discloses the return fields (chosen class, distribution, confidence, margin, auto-versus-review decision), explains the auto thresholds (0.85 probability and 0.50 margin) and that everything else is flagged for review, and clarifies that a manual_review class must be explicitly included—the tool never invents one. This gives agents a clear picture of behavior without 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 a single paragraph with logically ordered information: purpose, output, auto criteria, and a key behavioral note. It is information-dense without being verbose, and front-loads the core purpose before diving into details. Slightly long but appropriate for the tool's complexity.

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 tool has 6 parameters, no output schema, and no annotations, so the description must explain both usage and returns. It covers the return fields, auto decision logic, and the manual_review caveat. It does not address error handling, rate limits, or permission requirements, but for a classification tool these are minor. The description is sufficient for an agent to invoke it correctly.

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 coverage is 100%, so parameters are already fully documented. The description adds context about the batching and the independent Choice question mechanism, but does not add new semantic meaning beyond the schema's per-parameter descriptions. It reinforces that class descriptions are critical, but that is already stated in the schema. Thus, it meets the baseline but does not exceed it.

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 assigns each item to one class from a shared catalog in a single batched request. It uses a specific verb ('assign... to one class'), names the resource (items and class catalog), and distinguishes itself from siblings like decide or rerank by focusing on classification with a shared label set. The process and output are described in concrete terms.

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 provides detailed process information but does not explicitly guide when to use this tool versus alternatives. There is no mention of sibling tools or conditions that would favor a different tool (e.g., single decision, ranking, extraction). It only implicitly suggests use for batched classification, but lacks exclusions or alternative routing.

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