generate_image

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. The description only states what the tool does ('Generate images from text prompts') without mentioning any behavioral traits like rate limits, authentication requirements, cost implications, or what the output looks like. For a complex tool with 8 parameters and no annotations, this is a significant gap.

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 perfectly concise: two sentences that each earn their place. The first sentence states the core purpose, and the second provides essential usage guidance. There's zero waste or redundancy.

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 complexity (8 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (images in what format? URLs? base64?), doesn't mention cost or rate limit considerations, and provides minimal behavioral context. For an image generation tool with significant parameters, this leaves too many gaps.

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 description adds no parameter-specific information beyond what's in the input schema. With 75% schema description coverage (6 of 8 parameters have descriptions), the schema does most of the work. The description's reference to list_models for model selection provides some context for the model parameter, but doesn't add meaningful semantics beyond the schema's 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 clearly states the tool's purpose: 'Generate images from text prompts.' This is a specific verb+resource combination that distinguishes it from siblings like edit_image or generate_video. However, it doesn't explicitly differentiate from generate_image_from_image or generate_image_structured, which are also image generation tools.

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 clear context for usage: 'Use list_models with category='image' to discover available models.' This gives practical guidance on how to select the model parameter. However, it doesn't explicitly state when to use this tool versus alternatives like generate_image_from_image or generate_image_structured, nor does it provide exclusion criteria.

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