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

With no annotations, the description discloses payment workflow, stable endpoints, automatic model upgrades, and per-tier pricing. This covers key behavioral aspects like authentication and cost, though it omits error handling and response format details beyond 'returns JSON with image URL'.

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?

Eight sentences pack a lot of information without fluff, but the density makes it a wall of text. Content is relevant; could be restructured into bullets for readability.

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?

Covers the full workflow: payment prerequisite, generation from text or base64, model selection with costs, aspect ratio options, and stability guarantee. No output schema, but the return type (JSON with URL) is stated. Sufficient for a tool with 5 params.

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 covers 100% but the description adds critical meaning: maps modelId to named models with pricing and default, explains aspectRatio is free across tiers, clarifies imageBase64 enables img2img, and restates paymentId requirement. This exceeds the schema's basic descriptions.

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 clear verb-object pair ('Generate an image from a text prompt') and adds output format and model options. It differentiates from siblings like edit_image or analyze_image by focusing on net-new generation from text, and explicitly mentions img2img support.

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?

Provides explicit context: requires create_payment with toolName='generate_image', supports img2img, and payment is per-request. It does not explicitly name alternative tools or when not to use, but the context is clear for the intended use case.

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