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

Adds real behavioral context beyond annotations: credits are charged only on success, the call is asynchronous and yields a job id, and OAuth/API-key auth is spelled out with a concrete header format and settings URL. It does not address the destructiveHint=true annotation or whether repeated calls create separate billable jobs, which keeps it from a 5.

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?

Front-loads purpose and use cases before the quote/poll workflow, then auth last, which is the right ordering. Dense but every sentence carries operational weight; the parenthetical auth details are slightly bulky but justified for a paid, gated tool.

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 lifecycle an agent needs: auth setup, required quote, async job id, polling target, and billing timing. Return values are handled by the output schema, so nothing material is missing for correct invocation.

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 baseline is 3, but the description adds the constraint that quote_id must come from estimate_credit_cost for this exact model and that model must match the quote. It does not restate image_url's per-model allowlist, which the schema already covers in detail.

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?

Opens with a specific verb and resource ("Make an AI image") and enumerates concrete output types (product still, thumbnail, poster, concept art, reference frame). It also distinguishes itself from the async siblings by naming the polling tool get_generation, so an agent knows this starts a job rather than returning an image directly.

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?

Gives an explicit prerequisite chain: obtain quote_id from estimate_credit_cost first, then poll with get_generation (kind "image"). This is a when-to-use and how-to-sequence statement that routes the agent to the correct sibling tools rather than leaving order to inference.

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