analyze_text_emotion

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

With no annotations, the description carries the full burden. It discloses that the pipeline runs locally, which is a key behavioral trait, and lists the emotion labels. It does not detail performance or limitations, but the information is sufficient for a simple classification tool.

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?

Two sentences that front-load the main action and purpose. The first sentence is slightly long but clear. No superfluous information.

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 presence of an output schema, the description does not need to explain return values. It covers the input, the emotion categories, and a usage scenario. Basic edge cases are not addressed, but the tool's simplicity makes this acceptable.

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 single parameter 'text' has no schema description, and the schema coverage is 0%. The description adds context by referring to 'a block of text', but does not specify format, length, or encoding. It provides minimal added value beyond the type.

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 runs a deep learning pipeline to detect semantic emotional markers, lists the specific emotions (Joy, Sadness, Anger, Fear, Surprise, Disgust, Neutral), and distinguishes it from sibling tools that handle file operations, memory, or search.

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?

Explicitly suggests usage for adapting tone or understanding user sentiment, but does not provide explicit when-not-to-use or alternatives. The context from sibling tools makes its purpose distinct.

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