analyze_prompt

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits such as returning scores for specific criteria (clarity, specificity, structure, actionability) and providing improvement suggestions, which adds value beyond basic function. However, it lacks details on limitations, error handling, or performance aspects, leaving some behavioral context unspecified.

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 well-structured and front-loaded with the core purpose, followed by a bulleted list of usage guidelines and return value details. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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 tool's moderate complexity (2 parameters, no output schema), the description is mostly complete: it covers purpose, usage, and return values. However, without annotations or an output schema, it could benefit from more details on behavioral constraints or error cases, slightly limiting completeness for an analysis tool.

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%, so the schema already documents both parameters ('prompt' and 'evaluationCriteria') adequately. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples or usage nuances, resulting in a baseline score of 3 where the schema handles the heavy lifting.

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's purpose with specific verbs ('evaluate prompt quality', 'get actionable improvement suggestions') and distinguishes it from siblings like 'generate_prompt' and 'refine_prompt' by focusing on analysis rather than creation or refinement. It explicitly mentions what the tool does without being tautological.

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 description provides explicit usage scenarios in a bulleted list, including when to use it (e.g., 'assess if a prompt is well-structured', 'identify weaknesses before using a prompt') and implicitly when not to use it by contrasting with siblings like 'generate_prompt' for creation. It offers clear alternatives and context for tool selection.

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