categorize

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

With no annotations, the description carries the full burden. The bracketed '[conductor: verified stochastic command]' does hint at non-determinism, but it is cryptic metadata rather than plain disclosure, and nothing is said about return shape, error behavior when no category fits, or cost/latency.

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?

One front-loaded sentence that states the task and inputs without waste. The trailing bracketed tag is short but opaque, mildly reducing clarity-per-character.

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?

For a simple two-parameter tool with no output schema, the biggest remaining gap is what comes back: a category string, an index, or an object with a confidence score. That omission forces the agent to guess at the result contract.

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 0%, so the description must compensate. It does clarify both parameters semantically ('a piece of text', 'a provided list of categories'), but adds no detail on limits, ordering, or how ties between categories are broken.

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?

States a specific verb and resource ('choose the single best-fitting category') plus the input constraint ('from a provided list of categories'). It is clearly distinguishable from siblings like classify_sentiment or extract_keywords, though the description never explicitly contrasts itself with them.

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?

Usage is only implied: the agent learns it must supply text and a candidate category list, which signals the single-label classification scenario. There is no explicit statement of when to prefer this over related siblings (e.g. classify_sentiment for polarity) and no exclusions or call-order guidance.

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