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

With no annotations, the description carries the full burden and does so well: it discloses billing ($0.02/call over x402/USDC, charged only if the image is delivered), auth (no API key, no account), latency (~2 seconds), and output lifetime (24-hour URL). These are exactly the operational facts an agent needs before invoking.

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 dense sentences, fully front-loaded with the core capability and model, followed by output handling, then cost/auth. No 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?

No output schema exists, yet the description covers the return shape (hosted URL valid 24h, optional base64), timing, and pricing, so an agent knows what it gets and what it costs. Nothing material is missing.

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 steps, inline, and prompt. The description reiterates the step range/default and the inline behavior but adds no syntax or format detail beyond the schema, making 3 the correct baseline.

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 (Generates), resource (1024x1024 JPEG from a text prompt), and names the exact model FLUX.1 [schnell]. An agent can distinguish this from ai_speech, ai_transcribe, and ai_translate immediately.

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?

Explains the practical workflow ('hand straight to the next tool') and how to get base64 via inline=true, but never explicitly contrasts itself with sibling AI tools such as ai_speech. Context is clear, exclusions are absent.

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