Classify Text

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

Description only says it returns labels but does not disclose behavior on edge cases like no matches, multiple matches, or malformed rules. Annotations are minimal and do not add clarity.

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?

A single, tightly worded sentence with no filler. However, it may be too sparse, missing important context that would make it more useful.

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?

No output schema, so the description should clarify the exact return format (e.g., array of strings vs. single string). The phrase 'matching label(s)' is ambiguous about multiple matches and error behavior.

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% for all parameters, so the description adds little beyond what the schema already provides. It does not introduce new parameter 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?

Clearly states it classifies text against a set of rules and returns matching labels. This distinguishes it from sibling tools, none of which perform classification.

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 on when to use this tool versus alternatives (e.g., LLM-based classification or other extraction tools). Lacks any context or exclusions.

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