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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the pay-per-call model and the specific error behavior when the payment-signature header is missing (returns an error containing payment terms). It does not explicitly state read-only semantics or output handling, but the analysis nature makes that implicit. A 4 reflects that it goes beyond the bare minimum without describing every side effect.

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 concise (four sentences) and front-loads the primary purpose and usage before moving to alternatives and payment details. Every sentence earns its place—purpose, usage, alternatives, and a critical payment caveat—with zero wasted words.

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 simple analysis tool with two optional parameters and no output schema, the description covers purpose, usage boundaries, and the payment failure mode. It does not spell out the exact output structure, but it hints at what the result contains (sentiment, themes, triggers). Given the absence of annotations and output schema, this is reasonably complete, though a mention of the response format would push it to 5.

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 100%, and both parameters (query and context) are already well-described in the schema. The description adds little beyond the schema's own descriptions, but it does reinforce the purpose of the 'query' parameter by implying it holds the text to classify. Baseline 3 is appropriate when the schema handles parameter documentation.

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 opens with a specific verb ('Classify') and a defined resource ('overall sentiment of a text') and explicitly lists additional outputs ('key themes and emotional triggers'). It clearly distinguishes itself from sibling tools by naming extract-pain-points and analyze-review-velocity, so the agent can tell them apart without opening their schemas.

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?

It provides explicit when-to-use guidance ('Use for 'how do people feel about this'') and explicitly names alternative tools for different use cases ('For specific problems to fix, use extract-pain-points. For review momentum over time, use analyze-review-velocity.'). It also discloses a payment prerequisite, which is a concrete usage condition.

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