extract_emotions

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

In the absence of annotations, the description thoroughly covers side effects, authentication, rate limits, error handling, idempotency, and data privacy in a dedicated 'Behavioral Transparency' section. It is comprehensive and clearly communicates the tool's behavior.

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?

The description is well-structured with clear sections and front-loaded purpose. Some repetition occurs between the initial bullet points and the detailed 'Behavioral Transparency' section, but overall it is efficient and easy to read.

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?

Given the tool's simplicity (2 parameters, no nested objects, output schema present), the description covers all necessary aspects: purpose, usage, parameter semantics, and extensive behavioral details. It is fully complete for an agent to use correctly.

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?

The description provides brief parameter descriptions for text and api_key, but they add little beyond the schema (e.g., just restating name and type). With 0% schema coverage, the description barely compensates; the documentation is minimal.

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 clearly states the tool detects emotions in text and returns intensity scores. It distinguishes from siblings like analyze_sentiment by focusing specifically on emotions.

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 description includes dedicated 'When to use' and 'When NOT to use' sections, providing clear context for when to apply the tool. However, it does not explicitly compare to sibling tools like batch_analyze or compare_sentiments.

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