generate_image

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

With no annotations and no output schema, the description carries the full behavioral burden but discloses almost none of it: no return format (URL vs base64), no cost/latency or rate-limit notes, no statement of whether generated images are persisted. The only behavioral shading is a marketing claim about noise levels, which does not help an agent call the tool correctly.

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?

Front-loaded verb and resource, short overall, no filler. The trailing list of available models duplicates the model enum already present in the schema, so one clause is redundant.

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 generative tool with no annotations and no output schema, the agent still lacks essential information: what the call returns, whether multiple images (n up to 4) come back as a list, and any cost or timing expectations. The description covers invocation intent but leaves too much unknown.

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%, so the schema already documents all five parameters and their enums, making 3 the baseline. The description adds a rationale for the default model, but it conflicts with the schema's own rationale (description calls sunburst 'best value / lowest noise' while the schema field says 'highest quality'), mildly muddying rather than clarifying.

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?

States a specific verb and resource: 'Generate an image from a text prompt using high-quality AI models.' An agent can distinguish it from edit_image/create_variation by the text-to-image framing, though the description never names those siblings to make the boundary explicit.

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?

Usage is implied by 'generate an image from a text prompt', but there is no explicit statement of when to prefer this over edit_image or create_variation, nor any prerequisites or exclusions. The model-selection sentence is parameter guidance, not usage routing.

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