Provides tools to generate images from text prompts using OpenAI's image generation technology via the fal.ai platform, with support for customizing image size, quantity, quality, and background transparency.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Fal.ai OpenAI Image MCP Server Generate a high-quality image of a serene cyberpunk cityscape at sunset"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
fal.aiのGPT image-1 text-to-image APIを使用して画像を生成するFastMCPサーバーです。
依存関係をインストール:
npm install環境変数としてAPIキーを設定:
export FAL_KEY="your_fal_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"プロジェクトをビルド:
npm run buildサーバーを実行:
npm startRelated MCP server: SeedDream 4.0 FAL MCP Server
サーバーは以下のツールを提供します:
テキストプロンプトを使用して画像を生成します。
パラメーター:
prompt (必須): 生成する画像のテキスト説明
image_size (オプション): 生成する画像のサイズ ("auto", "1024x1024", "1536x1024", "1024x1536")
num_images (オプション): 生成する画像の数 (1-4)
quality (オプション): 生成する画像の品質 ("auto", "low", "medium", "high")
background (オプション): 背景のタイプ ("auto", "transparent", "opaque")
例:
{
"prompt": "夕暮れの静謐なサイバーパンク都市風景",
"image_size": "1024x1024",
"num_images": 1,
"quality": "high"
}Claude Codeでこのサーバーを使用するには、以下のJSONをMCP設定に追加してください:
{
"mcpServers": {
"fal-openai-image": {
"command": "node",
"args": ["/path/to/fal_openai_image1/dist/index.js"],
"env": {
"FAL_KEY": "your_fal_api_key_here",
"OPENAI_API_KEY": "your_openai_api_key_here"
}
}
}
}または、開発モードで実行する場合:
{
"mcpServers": {
"fal-openai-image": {
"command": "npm",
"args": ["run", "dev"],
"cwd": "/path/to/fal_openai_image1",
"env": {
"FAL_KEY": "your_fal_api_key_here",
"OPENAI_API_KEY": "your_openai_api_key_here"
}
}
}
}FAL_KEY: 必須。fal.ai API認証用のAPIキー
OPENAI_API_KEY: 必須。BYOK (Bring Your Own Key) システム用のOpenAI APIキー
Generate images using fal.ai GPT image-1 text-to-image API
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text description of the image to generate | |
| image_size | No | Size of the generated image | auto |
| num_images | No | Number of images to generate | |
| quality | No | Quality of the generated image | auto |
| background | No | Background type of the generated image | auto |
Does 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 mentions the API but fails to describe key behavioral traits like rate limits, authentication needs, response format, or potential side effects (e.g., cost implications). This leaves significant gaps for an AI agent to understand how to interact with the tool effectively.
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 that directly states the tool's function and API. It is front-loaded with the core action and avoids unnecessary details. However, it could be slightly more informative without sacrificing brevity, such as hinting at common use cases.
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 complexity of an image generation tool with 5 parameters and no output schema, the description is insufficient. It lacks information on what the tool returns (e.g., image URLs, metadata), error handling, or operational constraints. Without annotations or output schema, the description should provide more context to ensure the agent can use the tool correctly.
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?
The description does not add any meaning beyond the input schema, which has 100% coverage with detailed descriptions for all parameters. Parameters like 'prompt', 'image_size', and others are well-documented in the schema, so the description's lack of param info is acceptable. The baseline score of 3 reflects adequate schema coverage without extra value from the description.
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 action ('Generate images') and specifies the resource/API ('fal.ai GPT image-1 text-to-image API'), making the purpose evident. It lacks differentiation from siblings, but since there are no sibling tools, this is not a drawback. The description is specific enough to understand what the tool does without being tautological.
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 no guidance on when to use this tool, such as typical use cases, prerequisites, or alternatives. It simply states what the tool does without context for its application. Since there are no sibling tools, the lack of differentiation is not an issue, but overall usage guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
generate_imageScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'generate_image' has a clearly defined and distinct purpose for generating images via a specific API.
A single tool inherently has perfect naming consistency as there are no other tools to compare against. The name 'generate_image' follows a clear verb_noun pattern, which would be consistent if more tools were added.
A single tool is too few for a server with a domain like image generation, which could reasonably include operations like listing models, editing images, or fetching image details. This minimal set may cause agents to hit dead ends or lack flexibility in handling related tasks.
The server is severely incomplete for an image generation domain, covering only text-to-image generation. Obvious gaps include no tools for model management, image editing, retrieval of generated images, or error handling, which limits agent workflows and could lead to failures in broader tasks.
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