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

Annotations cover the safety profile (readOnly=false, openWorld=true, idempotent=false), and the description adds real behavioral context beyond them: the operation costs credits, is asynchronous (returns job_id), and requires explicit user confirmation. It does not restate non-idempotency or describe failure/credit-refund behavior.

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: what it does, how to retrieve results, and the cost/confirmation constraint. The credit warning is capitalized and front-loaded where it matters most.

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 12-parameter async mutation with no output schema, the description covers return shape (job_id), retrieval path, credit risk, and the confirmation gate. It leaves provider capability differences (e.g., xAI resolution limits) to the schema, which is reasonable but not fully self-contained.

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 67% and the schema itself carries rich enum and billing explanations (model, quality, resolution, thinking_level, reference URLs). The description only names the fields to confirm with the user, adding workflow meaning rather than new parameter semantics, so the baseline 3 applies.

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 (queue an AI image job) plus the backing service and provider options (Nano Banana/Gemini, Grok Imagine/xAI), and notes the return value. An agent can immediately distinguish this from generate_video and other siblings.

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?

Explicitly prescribes the follow-up workflow (poll list_jobs with type=image to get URLs) and a hard precondition (confirm subject + style + aspect_ratio + quality + provider with the user before calling). It does not name when to prefer this over generate_video or when not to use it, so it stops short of 5.

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