fal-mcp-server 1.11.0

MCP server for Fal.ai - Generate images, videos, music and audio with AI models

🎨 Fal.ai MCP Server

A Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.

✨ Features

🚀 Performance

• Native Async API - Uses fal_client.run_async() for optimal performance
• Queue Support - Long-running tasks (video/music) use queue API with progress updates
• Non-blocking - All operations are truly asynchronous

🌐 Transport Modes (New!)

• STDIO - Traditional Model Context Protocol communication
• HTTP/SSE - Web-based access via Server-Sent Events
• Dual Mode - Run both transports simultaneously

🎨 Media Generation

• 🖼️ Image Generation - Create images using Flux, SDXL, and other models
• 🎬 Video Generation - Generate videos from images or text prompts
• 🎵 Music Generation - Create music from text descriptions
• 🗣️ Text-to-Speech - Convert text to natural speech
• 📝 Audio Transcription - Transcribe audio using Whisper
• ⬆️ Image Upscaling - Enhance image resolution
• 🔄 Image-to-Image - Transform existing images with prompts

🔍 Dynamic Model Discovery (New!)

• 600+ Models - Access all models available on Fal.ai platform
• Auto-Discovery - Models are fetched dynamically from the Fal.ai API
• Smart Caching - TTL-based cache for optimal performance
• Flexible Input - Use full model IDs or friendly aliases

🚀 Quick Start

Prerequisites

• Python 3.10 or higher
• Fal.ai API key (free tier available)
• Claude Desktop (or any MCP-compatible client)

Installation

Option 1: uvx (Recommended - Zero Install) ⚡

Run directly without installation using uv:

# Run the MCP server directly
uvx --from fal-mcp-server fal-mcp

# Or with specific version
uvx --from fal-mcp-server==1.4.0 fal-mcp

Claude Desktop Configuration for uvx:

{
  "mcpServers": {
    "fal-ai": {
      "command": "uvx",
      "args": ["--from", "fal-mcp-server", "fal-mcp"],
      "env": {
        "FAL_KEY": "your-fal-api-key"
      }
    }
  }
}

Note: Install uv first: curl -LsSf https://astral.sh/uv/install.sh | sh

Option 2: Docker (Recommended for Production) 🐳

Official Docker image available on GitHub Container Registry.

Step 1: Start the Docker container

# Pull and run with your API key
docker run -d \
  --name fal-mcp \
  -e FAL_KEY=your-api-key \
  -p 8080:8080 \
  ghcr.io/raveenb/fal-mcp-server:latest

# Verify it's running
docker logs fal-mcp

Step 2: Configure Claude Desktop to connect

Add to your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "fal-ai": {
      "command": "npx",
      "args": ["mcp-remote", "http://localhost:8080/sse"]
    }
  }
}

Note: This uses mcp-remote to connect to the HTTP/SSE endpoint. Alternatively, if you have curl available: "command": "curl", "args": ["-N", "http://localhost:8080/sse"]

Step 3: Restart Claude Desktop

The fal-ai tools should now be available.

Docker Environment Variables:

Variable	Default	Description
FAL_KEY	(required)	Your Fal.ai API key
FAL_MCP_TRANSPORT	http	Transport mode: http, stdio, or dual
FAL_MCP_HOST	0.0.0.0	Host to bind the server to
FAL_MCP_PORT	8080	Port for the HTTP server

Using Docker Compose:

curl -O https://raw.githubusercontent.com/raveenb/fal-mcp-server/main/docker-compose.yml
echo "FAL_KEY=your-api-key" > .env
docker-compose up -d

Option 3: Install from PyPI

pip install fal-mcp-server

Or with uv:

uv pip install fal-mcp-server

Option 4: Install from source

git clone https://github.com/raveenb/fal-mcp-server.git
cd fal-mcp-server
pip install -e .

Configuration

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

2. Configure Claude Desktop by adding to:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json

For PyPI/pip Installation:

{
  "mcpServers": {
    "fal-ai": {
      "command": "fal-mcp",
      "env": {
        "FAL_KEY": "your-fal-api-key"
      }
    }
  }
}

Note: For Docker configuration, see Option 2: Docker above.

For Source Installation:

{
  "mcpServers": {
    "fal-ai": {
      "command": "python",
      "args": ["/path/to/fal-mcp-server/src/fal_mcp_server/server.py"],
      "env": {
        "FAL_KEY": "your-fal-api-key"
      }
    }
  }
}

3. Restart Claude Desktop

💬 Usage

With Claude Desktop

Once configured, ask Claude to:

• "Generate an image of a sunset"
• "Create a video from this image"
• "Generate 30 seconds of ambient music"
• "Convert this text to speech"
• "Transcribe this audio file"

Discovering Available Models

Use the list_models tool to discover available models:

• "What image models are available?"
• "List video generation models"
• "Search for flux models"

Using Any Fal.ai Model

You can use any model from the Fal.ai platform:

# Using a friendly alias (backward compatible)
"Generate an image with flux_schnell"

# Using a full model ID (new capability)
"Generate an image using fal-ai/flux-pro/v1.1-ultra"
"Create a video with fal-ai/kling-video/v1.5/pro"

HTTP/SSE Transport (New!)

Run the server with HTTP transport for web-based access:

# Using Docker (recommended)
docker run -d -e FAL_KEY=your-key -p 8080:8080 ghcr.io/raveenb/fal-mcp-server:latest

# Using pip installation
fal-mcp-http --host 0.0.0.0 --port 8000

# Or dual mode (STDIO + HTTP)
fal-mcp-dual --transport dual --port 8000

Connect from web clients via Server-Sent Events:

• SSE endpoint: http://localhost:8080/sse (Docker) or http://localhost:8000/sse (pip)
• Message endpoint: POST http://localhost:8080/messages/

See Docker Documentation and HTTP Transport Documentation for details.

📦 Supported Models

This server supports 600+ models from the Fal.ai platform through dynamic discovery. Use the list_models tool to explore available models, or use any model ID directly.

Popular Aliases (Quick Reference)

These friendly aliases are always available for commonly used models:

Alias	Model ID	Type
flux_schnell	fal-ai/flux/schnell	Image
flux_dev	fal-ai/flux/dev	Image
flux_pro	fal-ai/flux-pro	Image
sdxl	fal-ai/fast-sdxl	Image
stable_diffusion	fal-ai/stable-diffusion-v3-medium	Image
svd	fal-ai/stable-video-diffusion	Video
animatediff	fal-ai/fast-animatediff	Video
kling	fal-ai/kling-video	Video
musicgen	fal-ai/musicgen-medium	Audio
musicgen_large	fal-ai/musicgen-large	Audio
bark	fal-ai/bark	Audio
whisper	fal-ai/whisper	Audio

Using Full Model IDs

You can also use any model directly by its full ID:

# Examples of full model IDs
"fal-ai/flux-pro/v1.1-ultra"      # Latest Flux Pro
"fal-ai/kling-video/v1.5/pro"     # Kling Video Pro
"fal-ai/hunyuan-video"            # Hunyuan Video
"fal-ai/minimax-video"            # MiniMax Video

Use list_models with category filters to discover more:

• list_models(category="image") - All image generation models
• list_models(category="video") - All video generation models
• list_models(category="audio") - All audio models
• list_models(search="flux") - Search for specific models

📚 Documentation

Guide	Description
Installation Guide	Detailed setup instructions for all platforms
API Reference	Complete tool documentation with parameters
Examples	Usage examples for image, video, and audio generation
Docker Guide	Container deployment and configuration
HTTP Transport	Web-based SSE transport setup
Local Testing	Running CI locally with act

📖 Full documentation site: raveenb.github.io/fal-mcp-server

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Local Development

We support local CI testing with act:

# Quick setup
make ci-local  # Run CI locally before pushing

# See detailed guide
cat docs/LOCAL_TESTING.md

📝 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Fal.ai for providing the AI models
• Anthropic for the MCP specification