nanobanana_generate_image

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a generative operation ('creates high-quality images'), mentions quality dependencies ('more descriptive your prompt, the better the results'), and specifies the return format ('Task ID, trace ID, and generated image URL'). However, it doesn't cover potential limitations like rate limits, error conditions, or authentication needs, leaving some gaps.

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 well-structured and front-loaded, starting with the core purpose, followed by usage guidelines, tips, and return details. Each sentence earns its place by adding actionable information (e.g., prompt examples, when-to-use criteria) without redundancy. It's appropriately sized for a tool with two parameters and clear functionality.

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?

Given the tool's complexity (generative AI image creation), the description is complete enough. It covers purpose, usage, behavioral aspects, and return values, and with an output schema present, it doesn't need to explain return values in detail. The combination of description, schema (100% coverage), and output schema provides a comprehensive context for the agent.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds value by reinforcing the prompt's importance ('The more descriptive your prompt, the better the results') and providing detailed prompt writing tips with an example, which enhances understanding beyond the schema's technical descriptions. It doesn't add new parameter details but improves contextual usage.

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 clearly states the specific action ('Generate an AI image from a text prompt') and resource ('using Google's Nano Banana model'), distinguishing it from sibling tools like nanobanana_edit_image (which edits existing images) and nanobanana_get_task (which retrieves task status). It explicitly mentions creating high-quality images from detailed descriptions, establishing its unique purpose.

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 provides explicit usage scenarios with a bulleted list ('Use this when:') that includes when to use this tool (e.g., 'generate a new image from scratch') and implicitly when not to use it (e.g., for editing existing images, which is handled by nanobanana_edit_image). It also offers prompt writing tips, further guiding effective usage.

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