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

Adds substantial behavior beyond the annotations: async jobId return, ~1-3 minute latency, per-type credit costs, automatic refunds on failure, and silent per-slot skipping. The destructiveHint=true is sensibly explained by the credit-charging language, though the description never explicitly frames credit spend as the destructive aspect, leaving that inference to the reader.

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?

Opens with the core action, then layers modes, cost, optionality, and async caveats in a single dense paragraph. Despite its length, every clause conveys operational information an agent needs, and non-obvious pitfalls (silent skips, refunds) are front-loaded where relevant.

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 4-parameter, async, credit-consuming, open-world generation tool this leaves nothing material unstated: it covers mode selection, cost, refund behavior, polling, latency, and partial-result risk. With an output schema present, the description does not need to document return fields and rightly skips them.

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 already 100%, yet the description still adds real meaning: count is bounded 1-5 and cautioned that fewer may land, brandId's omit-vs-pass semantics are spelled out, each enum value carries a credit cost and behavior, and prompt is illustrated with concrete source examples (changelog line, blog title, launch note).

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 (create) and resource (memes) plus the input modality (plain-language prompt) and enumerates the three distinct modes. An agent can immediately tell this apart from studio_generate_image, seedance_generate_image, and create_slides, which are the nearest media-generation 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 describes when to choose each memeType (edit vs greenscreen vs comic), when to include brandId (with a pointer to list_brands) versus omit it, and how to operate the async flow (poll get_job). It even pre-empts the common confusion that the caller must supply the joke.

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