sentiment

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

With no annotations, the description carries the full burden and does disclose meaningful traits: VADER produces a valence-lexicon compound score, stanza is a trained neural per-sentence classifier, and auto selects the best installed engine. It omits failure behavior when a model isn't installed, but the engine characteristics are useful beyond what any structured field provides.

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?

Three tight sentences, front-loaded with the core purpose, then per-language engine detail, then the auto recommendation. Little waste, though the parenthetical engine detail could be marginally trimmed.

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?

An output schema exists, so return values needn't be explained, and the description covers the language/engine landscape well. The gap is accepted enum values for lang and engine, which matters for a 0%-coverage schema but is a modest omission overall.

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 0%, so the description must compensate for all three parameters. It implies lang values (English, Chinese) and engine behavior (auto), but never enumerates valid lang/engine strings or explains the required text parameter, leaving agents to guess accepted values.

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 states a specific verb and resource (sentiment classification) and names the pretrained engines used for each language, which clearly separates it from the other text-analysis siblings. It stops short of explicitly contrasting with annotate_full, but the purpose is unambiguous.

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 only actionable guidance is 'Use auto to get the best installed engine,' which covers engine selection but not when to prefer this tool over annotate_full or when a specific language/engine is required. Usage is implied by the language notes rather than stated.

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