Generate an image from a detailed text prompt with adjustable aspect ratios using the Nano Banana model.
Generate an image using the Nano Banana (gemini-2.5-flash-image) model.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Highly detailed description of the image to generate. | |
| aspectRatio | No | Aspect ratio for the image (e.g., '1:1', '16:9', '9:16', '4:3', '3:4') | 1:1 |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose return format (URL, binary, base64), latency, cost, content restrictions, or any other behavioral traits beyond naming the model, which is insufficient for a generative 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 a single efficient sentence with no wasted words and is front-loaded with the core action. However, its brevity contributes to under-specification, which is penalized in other dimensions.
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 generates an image and has no output schema or annotations. The description should explain what is returned (e.g., image URL or binary data) and any important behavioral details, but it does none of that, leaving the agent without essential context for 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%, and both parameters are fully documented in the input schema, so the baseline score is 3. The description adds no parameter-specific meaning or constraints beyond what the schema already provides.
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 states a specific verb and resource ("Generate an image") and names the underlying model. It is clear what the tool does, though there are no sibling tools to differentiate against.
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?
There is no guidance on when to use this tool, when not to use it, or what alternatives exist. The description only states the operation, leaving the agent to infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.