Create images from natural language prompts using Fal AI's nano-banana-pro model. Set aspect ratio, image count, and output format to match your needs.
Fal AI (fal-ai/nano-banana-pro) を使用して画像を生成します。
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | 生成する画像のプロンプト (英語推奨) | |
| num_images | No | 生成する画像の枚数 | |
| aspect_ratio | No | アスペクト比 (例: '1:1', '16:9') | 1:1 |
| output_format | No | 出力フォーマット (例: 'png', 'jpeg') | png |
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 of behavioral disclosure. It reveals that generation happens through an external provider (Fal AI) and names the model, which is genuine added context, but it says nothing about latency, cost, rate limits, or failure modes for what is clearly a remote generation call.
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?
A single short, front-loaded sentence with no filler. It is efficient, though the extreme brevity borders on under-specification for a generative tool rather than being a model of well-structured conciseness.
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?
There is no output schema, no annotations, and four parameters, yet the description never states what the tool returns (image URL, base64, file reference) or where results land. For a tool with zero structured behavioral coverage, this leaves a significant gap an agent would need in order to use the result.
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 all four parameters (prompt, num_images, aspect_ratio, output_format) are already documented in the schema, including defaults and examples. The description adds nothing beyond that, making the baseline 3 appropriate.
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 names a specific verb (生成します) and resource (画像) and even identifies the backing model/provider (fal-ai/nano-banana-pro), so the agent knows exactly what the tool does. There are no sibling tools to differentiate from, so the only missing element is the differentiation clause that a 5 would require.
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 gives no guidance on when to use this tool versus alternatives, no prerequisites, and no indication of when it would be inappropriate (e.g., cost, latency, external API dependency). It states only what the tool does, leaving all usage context to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.