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

With no annotations, the description carries the full burden of disclosing behavior. It does disclose the return structure ({score, label, confidence}), which adds useful information, but it does not explain the meaning of 'score' or 'confidence', nor any edge cases or error 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 two short sentences, front-loaded with purpose and output format. Every word earns its place with no redundancy or fluff.

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 one-parameter tool with no output schema, the description provides the return structure but lacks interpretive context for the score and confidence values. It also offers no guidance on appropriate use cases, making it minimally complete.

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 schema has one parameter 'text' with 0% description coverage. The description's 'of text' clarifies that the parameter is the text to be analyzed, adding some meaning beyond the bare schema. However, it does not detail format constraints, length limits, or language expectations.

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 analyzes sentiment of text and lists the output categories (positive/negative/neutral), using a specific verb and resource. It distinguishes from sibling tools like detect_language and summarize_text by focusing specifically on sentiment classification.

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 guidance on when to use this tool versus alternatives. It does not provide context, exclusions, or alternate tool suggestions. The agent must infer usage solely from the purpose statement.

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