classify_sentiment

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

No annotations are provided, so the description carries the full burden. Beyond naming the three labels, it says nothing about language support, text-length limits, handling of mixed or neutral-ambiguous text, or whether results are deterministic — indeed the trailing artifact '[conductor: verified stochastic command]' hints at nondeterminism without explaining it.

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 states the purpose and the label vocabulary with no wasted words. The bracketed conductor artifact is noise that does not belong in an agent-facing description.

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 single-input classification tool with no output schema and no annotations, the description does the minimum: it names the labels. It omits any guidance on input constraints, language coverage, or return shape, which leaves real gaps given that nothing else in the tool definition fills them.

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?

There is a single parameter, text, documented at 0% schema coverage, but its meaning is largely self-evident from the tool purpose. The description adds value by revealing the output label set, which partially compensates for the absent output schema, though it says nothing about format, length, or language constraints on the input.

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 gives a specific verb (classify) and resource (sentiment of a piece of text) and enumerates the possible output labels, so the agent knows exactly what the tool produces. It does not explicitly distinguish itself from the nearest sibling, categorize, but the sentiment-specific framing is clear enough on its own.

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?

There is no statement of when to use this versus categorize or extract_keywords, no prerequisites, and no exclusions. The agent must infer the usage context entirely from the tool name and one-sentence purpose.

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