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fal.ai MCP Server

@am0y

Repository

Basic information

Category

Other

License

MIT

Transports

stdio

Author

am0y

Submitted by

wize bg

About fal.ai MCP Server

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

fal.ai MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

fal.ai MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

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`).

Comments

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