# 🎨 Fal.ai MCP Server

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

<a href="https://glama.ai/mcp/servers/@raveenb/fal-mcp-server">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/@raveenb/fal-mcp-server/badge" alt="Fal.ai Server MCP server" />
</a>

## ✨ 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](https://fal.ai) (free tier available)
- Claude Desktop (or any MCP-compatible client)

### Installation

#### Option 1: uvx (Recommended - Zero Install) ⚡

Run directly without installation using [uv](https://docs.astral.sh/uv/):

```bash
# Run the MCP server directly
uvx fal-mcp-server

# Or with specific version
uvx fal-mcp-server==1.2.0
```

**Claude Desktop Configuration for uvx:**
```json
{
  "mcpServers": {
    "fal-ai": {
      "command": "uvx",
      "args": ["fal-mcp-server"],
      "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:

```bash
# Pull the latest image
docker pull ghcr.io/raveenb/fal-mcp-server:latest

# Run with your API key (uses sensible defaults)
docker run -d \
  --name fal-mcp \
  -e FAL_KEY=your-api-key \
  -p 8080:8080 \
  ghcr.io/raveenb/fal-mcp-server:latest
```

**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 |

```bash
# Example with custom configuration
docker run -d \
  --name fal-mcp \
  -e FAL_KEY=your-api-key \
  -e FAL_MCP_TRANSPORT=http \
  -e FAL_MCP_HOST=0.0.0.0 \
  -e FAL_MCP_PORT=8080 \
  -p 8080:8080 \
  ghcr.io/raveenb/fal-mcp-server:latest
```

Or use Docker Compose:
```bash
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

```bash
pip install fal-mcp-server
```

Or with uv:
```bash
uv pip install fal-mcp-server
```

#### Option 4: Install from source

```bash
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](https://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 Docker Installation:
```json
{
  "mcpServers": {
    "fal-ai": {
      "command": "curl",
      "args": ["-N", "http://localhost:8080/sse"]
    }
  }
}
```

#### For PyPI Installation:
```json
{
  "mcpServers": {
    "fal-ai": {
      "command": "python",
      "args": ["-m", "fal_mcp_server.server"],
      "env": {
        "FAL_KEY": "your-fal-api-key"
      }
    }
  }
}
```

#### For Source Installation:
```json
{
  "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:

```bash
# 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](docs/docker.md) and [HTTP Transport Documentation](docs/HTTP_TRANSPORT.md) 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:

```python
# 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

## 🤝 Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

### Local Development

We support local CI testing with `act`:

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

# See detailed guide
cat docs/LOCAL_TESTING.md
```

## 📝 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- [Fal.ai](https://fal.ai) for providing the AI models
- [Anthropic](https://anthropic.com) for the MCP specification