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

With no annotations, the description carries the behavioral transparency burden and does so thoroughly. It discloses the JSON return format, model-specific costs, default model behavior, img2img support, stable endpoints with automatic model upgrades, Bitcoin Lightning payment, and no API key/signup requirement.

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?

Although longer than many descriptions, every sentence earns its place: purpose and return type are front-loaded, then models/pricing, parameter nuances, pricing stability, and the payment prerequisite. The structure is dense without irrelevant filler.

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 paid, 5-parameter tool with no output schema and no annotations, the description is largely complete: it covers return shape, model selection, costs, defaults, img2img, aspect ratio effects, auth/payment, and stability guarantees. It only omits potential async/retrieval or error-handling behavior, which would make it fully complete.

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 input schema already describes every parameter with high coverage, so the baseline is 3. The description adds meaningful extra context beyond the schema by providing model names, pricing, the default model default, aspect-ratio price neutrality, and the payment prerequisite, which helps an agent choose and invoke parameters correctly.

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 opens with a specific verb and resource: "Generate an image from a text prompt," and states the return value as JSON with an image URL. It is clear about text-to-image and img2img support, though it does not explicitly differentiate itself from sibling tools like edit_image or generate_text.

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?

It provides strong usage context: when to use the tool, model selection guidance, default model behavior, pricing tiers, and the prerequisite that create_payment must be called first. It does not give explicit exclusions or say when to choose an alternative sibling instead.

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