nano_banana_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 discloses the important async polling behavior for 4K requests and GCS uploads, but does not cover auth requirements, rate limits, or the exact nature of the return value (beyond polling task). This is partial but not exhaustive transparency.

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 three sentences, each earning its place: the first states the core purpose, the second provides parameter guidance, and the third discloses an edge-case behavioral trait. It is front-loaded and free of padding.

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 high complexity (18 parameters, multiple reference input methods, GCS interactions) and no output schema, the description should explain more about return values and workflows. It mentions the polling task for 4K but does not describe what the default return payload looks like or how to sequence with nano_banana_get_task. This leaves gaps, so a score of 3 is appropriate.

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 baseline is 3. The description adds value by recommending referenceImagePaths or referenceImageUris over referenceImages (base64) and by explaining the implications of imageSize=4K. These details go beyond the schema field descriptions, helping the agent choose parameters wisely.

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 tool's function: 'Generate images with Gemini 3 Pro Image on Vertex AI and upload results to GCS.' It specifies the action (generate), the resource (Gemini 3 Pro Image on Vertex AI), and the downstream effect (upload to GCS), fully distinguishing it from the sibling tool nano_banana_get_task which retrieves tasks.

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 clear usage context by advising 'Prefer referenceImagePaths or referenceImageUris to avoid base64' and explaining the async behavior for 4K imageSize requests. It does not explicitly mention when not to use the tool or name alternatives beyond the implicit sibling relationship, but the guidance is actionable and context-rich.

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