generate_image

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

No annotations are present, so the description carries the full disclosure burden. It usefully reveals that output is added as a new layer and that generation runs either locally (ComfyUI) or via a remote service, but it omits side effects on the document, auth/key requirements for online backends, latency, cost, and failure behavior for what is clearly a mutating operation.

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?

A single front-loaded sentence with dense parenthetical detail and no filler. Very efficient, though the parenthetical backend list is long enough that readability could suffer slightly.

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 8 parameters, 0% schema description coverage, no annotations, and no output schema, the description should do substantially more. It covers the core generation concept and two parameter families but leaves critical parameters and all behavioral/authorization context unaddressed, so it is only marginally sufficient for a tool of this complexity.

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 0%, so the description must compensate. It adds real value by enumerating backend values and fit options (cover/contain) that the schema leaves as bare strings, and implies 'prompt'. But it is silent on seed, width, height, negative, and project — half of the 8 parameters remain undocumented anywhere.

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 ('Text-to-image') plus the mechanism and output placement ('placed on a new layer'). This inherently distinguishes it from siblings like add_image (imports an existing image) and edit_image/inpaint (transform existing content) without needing to name them.

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?

Implies usage by naming backends (backend=openai, gemini, stability, openrouter, replicate) and fit modes, so an agent can infer how to select among them. However, there is no explicit when-to-use guidance, no when-not-to-use, and no routing to alternatives such as inpaint or edit_image for modifying existing content. Usage is implied, not directed.

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