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fal.ai MCP Server

@am0y

代码仓库

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

am0y

提交者

wize bg

关于 fal.ai MCP Server

A Model Context Protocol (MCP) server for interacting with fal.ai models and services.

fal.ai MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-fal": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

fal.ai MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is fal.ai MCP Server?

fal.ai MCP Server is a Model Context Protocol (MCP) server that enables interaction with fal.ai models and services. It allows you to list, search, generate content, manage queues, and upload files to fal.ai’s CDN, all through a standardized MCP interface. It is intended for developers using MCP-compatible clients like VS Code, Claude Desktop, or Antigravity.

How to use fal.ai MCP Server?

Install Python 3.10+, clone the repository, create a virtual environment, and install dependencies from requirements.txt. Set the FAL_KEY environment variable with your fal.ai API key. Then configure your MCP client (e.g., Claude Desktop, VS Code) to point to the venv’s Python interpreter and the main.py script. Use the provided tools (models, search, generate, result, etc.) to interact with fal.ai models.

Key features of fal.ai MCP Server

  • List all available fal.ai models with optional pagination
  • Search for specific models by keywords
  • Retrieve OpenAPI schemas for any model
  • Generate content using any fal.ai model (direct or queued)
  • Manage queued requests (check status, get results, cancel)
  • Upload files to fal.ai CDN

Use cases of fal.ai MCP Server

  • Generate images, text, or other AI content via fal.ai models directly from an MCP client
  • Search and explore the fal.ai model catalog programmatically
  • Automate queue‑based inference workflows with status polling and cancellation
  • Upload generated assets (e.g., images, videos) to fal.ai CDN for sharing or further processing

常见问题

1

What Python version and dependencies are required?

Python 3.10+ is required. Dependencies include `fastmcp`, `httpx`, and `aiofiles`.

2

Why doesn’t Docker work for MCP integration?

MCP servers use stdio transport and must be spawned as a child process by the client. Docker containers lack an active stdin connection, so the server exits immediately. Direct Python execution is the only supported approach.

3

How do I set my fal.ai API key?

Set the `FAL_KEY` environment variable, or create a `.env` file in the project root with `FAL_KEY=your_key_here`. The server loads it automatically.

4

Which MCP clients are supported?

The server is designed for Claude Desktop, VS Code, and Antigravity. Configuration JSON must point to the venv Python interpreter and the `main.py` script.

5

What tools does the server expose?

The server provides tools for listing models (`models`), searching (`search`), getting schemas (`schema`), generating content (`generate`), managing queues (`result`, `status`, `cancel`), and uploading files (`upload`).

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