generate_image

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

With no annotations, the description carries full burden. It discloses that generation runs on the user's Google account, which is useful context, but it omits key behavioral details such as whether images are returned directly or saved, whether calls are synchronous or asynchronous, and any rate limits or costs. This is a significant gap for a generation tool.

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?

The description is two short sentences, front-loads the core action, and every word contributes. It is appropriately compact.

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?

The tool has 7 parameters and no output schema, yet the description provides only the basic function and account context. It fails to explain what the tool returns, how it relates to sibling tools like batch_generate_images, or any post-generation steps, making it incomplete for efficient selection and 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 descriptions cover all 7 parameters, including enums for model and aspect ratio, so the description adds no additional parameter-level meaning. The baseline of 3 applies due to high schema coverage.

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 uses a specific verb ('Generate'), names the resource ('AI image'), and identifies the underlying system ('Google Flow') and models ('Nano Banana / NARWHAL'), clearly distinguishing this from video or upscaling tools.

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?

The description implies use when an AI image is needed from a prompt, but it does not provide explicit guidance on when to choose this over siblings like batch_generate_images, generate_from_dossier, or upscale_image. No exclusions or alternatives are mentioned.

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