optimize_prompt

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

With no annotations provided, the description carries the full burden and explicitly discloses the optimizer's behavior (reordering tags, moving quality tags, adding missing categories). It doesn't cover edge cases like both inputs being provided, but the primary transformations are transparent.

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 efficiently structured: a one-line purpose, a one-line input instruction, and a concise bullet list of actions. Every element earns its place, and the purpose is front-loaded.

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?

With an output schema present and only two optional parameters, the description covers the essential context: input modes and processing steps. It omits potential conflict behavior (e.g., if both inputs are provided), but this is a minor gap given the tool's simplicity.

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 the description adds value by clarifying that 'prompt' and 'description' are alternative inputs ('Provide either...') and by explaining how each feeds into the optimization process. This goes beyond the schema's simple field descriptions.

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 a specific action ('Optimize a prompt') and the target model ('Animagine XL'), then lists concrete transformation steps. This differentiates it from siblings like validate_prompt and explain_prompt by specifying exactly what optimization entails.

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 gives clear input options ('Provide either a natural language description or an existing prompt') and implies the use case of improving prompt structure. It doesn't explicitly mention when not to use it or alternatives, but the context is clear enough.

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