score_prompt

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

No annotations provided, so description carries full burden. It discloses the return value in detail (ScoreCard with overall, grade, breakdown, missing, anti-patterns, verdict) and lists aliases, adding behavioral context beyond the schema.

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?

Description is concise and front-loaded, stating purpose and return value first, then listing supported models. Every sentence adds value with no repetition 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 tool with 2 simple parameters and an output schema, the description covers purpose, return details, supported models, and aliases. It fully addresses what an agent needs to invoke 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?

Schema coverage is 100%, but description adds value by listing supported models and aliases for the 'model' parameter. For 'prompt', it merely restates the schema. The model list is significant additional semantics.

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?

Description clearly states it scores a prompt for a generation model (0–100) and returns a detailed ScoreCard. It lists supported models and aliases, distinguishing it from siblings like score_and_enhance or score_variations.

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?

Description specifies what the tool does and its return format, but does not explicitly guide when to use this tool versus alternatives like score_and_enhance or score_variations. The list of supported models provides context but lacks explicit when-not or alternative recommendations.

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