fal

derekalia/fal

3.3

If you are the rightful owner of fal and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

A Model Context Protocol (MCP) server for interacting with fal.ai models and services, inspired by am0y's MCP server but updated for streaming MCP support.

Tools

  1. models

    List available models with optional pagination

  2. search

    Search for models by keywords

  3. schema

    Get OpenAPI schema for a specific model

  4. generate

    Generate content using a model

  5. result

    Get result from a queued request

  6. status

    Check status of a queued request

  7. cancel

    Cancel a queued request

  8. upload

    Upload a file to fal.ai CDN

fal MCP Server

A Model Context Protocol (MCP) server for interacting with fal.ai models and services. This project was inspired by am0y's MCP server, but updated to use the latest streaming MCP support.

Features

  • List all available fal.ai models
  • Search for specific models by keywords
  • Get model schemas
  • Generate content using any fal.ai model
  • Support for both direct and queued model execution
  • Queue management (status checking, getting results, cancelling requests)
  • File upload to fal.ai CDN
  • Full streaming support via HTTP transport

Requirements

  • Python 3.12+
  • fastmcp
  • httpx
  • aiofiles
  • A fal.ai API key

Installation

  1. Clone this repository:
git clone https://github.com/derekalia/fal.git
cd fal
  1. Install the required packages:
# Using uv (recommended)
uv sync

# Or using pip
pip install fastmcp httpx aiofiles

Usage

Running the Server Locally

  1. Get your fal.ai API key from fal.ai

  2. Start the MCP server with HTTP transport:

./run_http.sh YOUR_FAL_API_KEY

The server will start and display connection information in your terminal.

  1. Connect to it from your LLM IDE (Claude Code or Cursor) by adding to your configuration:
{
  "Fal": {
    "url": "http://127.0.0.1:6274/mcp/"
  }
}

Development Mode (with MCP Inspector)

For testing and debugging, you can run the server in development mode:

fastmcp dev main.py

This will:

  • Start the server on a random port
  • Launch the MCP Inspector web interface in your browser
  • Allow you to test all tools interactively with a web UI

The Inspector URL will be displayed in the terminal (typically http://localhost:PORT).

Environment Variables

The run_http.sh script automatically handles all environment variables for you. If you need to customize:

  • PORT: Server port for HTTP transport (default: 6274)
Setting API Key Permanently

If you prefer to set your API key permanently instead of passing it each time:

  1. Create a .env file in the project root:
echo 'FAL_KEY="YOUR_FAL_API_KEY_HERE"' > .env
  1. Then run the server without the API key argument:
./run_http.sh

For manual setup:

  • FAL_KEY: Your fal.ai API key (required)
  • MCP_TRANSPORT: Transport mode - stdio (default) or http

Available Tools

  • models(page=None, total=None) - List available models with optional pagination
  • search(keywords) - Search for models by keywords
  • schema(model_id) - Get OpenAPI schema for a specific model
  • generate(model, parameters, queue=False) - Generate content using a model
  • result(url) - Get result from a queued request
  • status(url) - Check status of a queued request
  • cancel(url) - Cancel a queued request
  • upload(path) - Upload a file to fal.ai CDN

License