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

No annotations are provided, so the description carries the burden. It discloses the return type (winner and token delta) but does not mention side effects, safety, or computation details. The partial transparency is helpful but lacks deeper behavioral context.

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 extremely concise, consisting of two short sentences that state the core function and expected output. Every word earns its place, with no redundancy or filler.

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 two-parameter comparison tool, the description adequately covers the purpose and output. It lacks usage guidance and details on how clarity score is computed, but given the tool's simplicity, it is reasonably complete. The absence of an output schema makes the return description important, and it is provided.

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 no parameter descriptions (0% coverage). The description clarifies that prompt_a and prompt_b are the two prompts being compared, which adds meaning, but it does not elaborate on ordering, validation, or interpretation of these parameters beyond their names.

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 compares two prompts and specifies the output (winner by clarity score and token delta). It is distinct from sibling tools like analyze_prompt (single prompt analysis) and estimate_tokens (token counting).

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 implication is clear: use this when you need to compare two prompts. However, the description does not explicitly state when to use this over alternatives like analyze_prompt or optimize_prompt, nor does it provide exclusions or alternative recommendations.

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