netintel_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. It discloses the output dimensions (polarity, score range, emotions, optional aspects), which is useful, but omits key behavioral traits: whether it is a paid/compute-heavy LLM call, latency, rate limits, language support, or text-length limits. Partial credit for output disclosure.

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?

Single front-loaded sentence that gives purpose and return shape efficiently. Slightly overloaded with enumerated return fields, and it ends mid-thought with an ellipsis, but nothing is wasted.

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 and no annotations, so the description should do more. It does convey the return shape, which is the biggest gap-filler, but leaves parameters, performance, and language constraints unspecified for a tool with two undeclared parameters.

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% and the description only vaguely alludes to 'per-aspect sentiment' without naming or explaining the aspects array parameter, and says nothing about the text parameter's format or limits. With two undocumented parameters, the description fails to compensate for the coverage gap.

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+resource (analyze sentiment of text) and names the underlying model (Claude Haiku) plus the return shape (polarity, -1 to +1 score, emotions, per-aspect). Distinct from siblings like netintel_classify and netintel_content_moderate, though it does not explicitly name those alternatives.

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?

Implied usage is given via the example use cases ('gauge tone in reviews, messages'), which hints at context, but there is no explicit when-to-use vs alternatives guidance, no exclusions, and no mention of how it differs from netintel_classify or netintel_content_moderate.

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