Generate an image

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

Annotations already declare openWorldHint=true, idempotentHint=false, and destructiveHint=false, so the description's value is in the extra detail it supplies: fixed per-image pricing, images returned inline and/or written to CARPE_OUTPUT_DIR, and automatic queuing of slow models. The async disclosure is the most important addition, though it omits how to follow up on a returned job_id.

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 compact sentences, front-loaded with the core action and followed by output location, cost, and queuing behavior in descending order of importance. The model-list parenthetical is dense but earns its place by signaling capability breadth.

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 16-parameter generation tool with no output schema, the description correctly covers the return channel and the queue path. The key gap is that it mentions a job_id being returned after wait_seconds without telling the agent to poll carpe_image_status, leaving the async workflow incomplete.

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?

With 16 parameters and 69% schema description coverage, the schema already documents the important knobs (mode, model, route, format, quality, variants, safe_mode, resolution, aspect_ratio, wait_seconds, extra). The description adds only marginal meaning — default model families, default tiering behavior, pricing implications — and says nothing about the undocumented prompt, seed, width, height, or negative_prompt params.

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 image(s) from a prompt") and enumerates the serving model families, so an agent immediately knows this is text-to-image creation rather than editing or upscaling. Sibling tools like carpe_image_edit and carpe_image_upscale are not named explicitly, but the verb distinction is strong enough to disambiguate.

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 routes the agent usefully in two places: it points to carpe_pricing for cost and to carpe_list_models for model ids, and it explains that slow models are auto-queued. However, it never states when to prefer this over carpe_image_edit, carpe_image_multi_edit, or carpe_image_upscale, and gives no when-not-to-use conditions.

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