generate_image

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

With no annotations, the description discloses fallback on quota/availability errors, return format as base64 for direct rendering, and no API key requirement. It could mention failure behaviour if all models fail, but overall 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?

Three sentences, each adding value: purpose, features/fallback, return format and auth. No redundancy, front-loaded with core functionality.

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 no output schema, the description explains return format adequately. It covers purpose, models, fallback, auth. Slight gap: no explicit mention of output structure beyond base64, but sufficient for agent.

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 advising 'Be detailed and specific' for prompt, explaining different quality options per model, and recommending list_models for model options.

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 generates an image from text prompt, lists supported models, and distinguishes from sibling 'list_models' which lists models instead of generating images.

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 implies when to use this tool (generation) vs alternative 'list_models' (discovery). It references list_models in the schema for model selection, providing cross-reference. No explicit exclusions but clear context.

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