mcp-fal
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-fal Generate a cute corgi dog on a beach"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-fal
An MCP (Model Context Protocol) server that exposes Fal.ai generative media models as tools callable by Claude and other MCP-compatible clients.
Features
Tool | Description |
| Generate images from a text prompt using FLUX, Stable Diffusion, and other models |
| Generate videos via text-to-video or image-to-video using MiniMax, HunyuanVideo, and more |
| Run any Fal.ai model by ID with arbitrary input — access to 1000+ models |
Related MCP server: @monsoft/mcp-fal-ai
Installation
# Clone the repository
git clone https://github.com/mohsenmousavieyeline/mcp-fal.git
cd mcp-fal
# Install dependencies
npm install
# Build (compiles TypeScript → dist/)
npm run buildAPI Key
Get your Fal.ai API key at https://fal.ai/dashboard/keys.
The server resolves the API key in this priority order:
CLI argument —
--api-key <your-key>Environment variable —
FAL_KEY=<your-key>
Configuration
Claude Desktop
Add the server to your Claude Desktop config file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Option A — API key as CLI argument (recommended):
{
"mcpServers": {
"fal-ai": {
"command": "node",
"args": [
"/absolute/path/to/mcp-fal/dist/index.js",
"--api-key",
"fal_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
]
}
}
}Option B — API key as environment variable:
{
"mcpServers": {
"fal-ai": {
"command": "node",
"args": ["/absolute/path/to/mcp-fal/dist/index.js"],
"env": {
"FAL_KEY": "fal_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}Claude Code (CLI)
claude mcp add fal-ai -- node /absolute/path/to/mcp-fal/dist/index.js --api-key fal_xxxxOr with an environment variable:
FAL_KEY=fal_xxxx claude mcp add fal-ai -- node /absolute/path/to/mcp-fal/dist/index.jsnpx (no installation)
If published to npm:
{
"mcpServers": {
"fal-ai": {
"command": "npx",
"args": ["mcp-fal", "--api-key", "fal_xxxx"]
}
}
}Tool Reference
generate_image
Generate one or more images from a text prompt.
Parameter | Type | Required | Default | Description |
| string | ✅ | — | Text description of the image |
| string | — |
| Fal.ai model ID |
| string | — |
|
|
| integer | — |
| Number of images (1–4) |
| string | — | — | What to exclude from the image |
| integer | — | — | Seed for reproducible results |
Example prompt to Claude:
"Generate an image of a misty mountain landscape at dawn using FLUX schnell"
generate_video
Generate a video from text or an input image.
Parameter | Type | Required | Default | Description |
| string | ✅ | — | Text description of the video |
| string | — | Auto-selected | Fal.ai model ID |
| string | — | — | Input image URL (triggers image-to-video) |
| number | — | — | Desired duration in seconds |
Default models:
With
image_url:fal-ai/minimax-video/image-to-videoWithout
image_url:fal-ai/minimax-video/text-to-video
⚠️ Video generation typically takes 1–5 minutes.
Example prompt to Claude:
"Generate a 5-second video of ocean waves crashing on a rocky shore"
run_model
Run any Fal.ai model with custom parameters.
Parameter | Type | Required | Description |
| string | ✅ | Fal.ai model ID (e.g. |
| object | ✅ | Model-specific input parameters |
Browse available models at https://fal.ai/models.
Example prompt to Claude:
"Use the run_model tool with fal-ai/imageutils/rembg to remove the background from this image: https://example.com/photo.jpg"
Development
# Run in development mode (no build step required)
FAL_KEY=your_key npm run dev
# Build for production
npm run build
# Run the built server
FAL_KEY=your_key npm startPopular Fal.ai Models
Category | Model ID | Description |
Image |
| FLUX.1 Dev — high quality |
Image |
| FLUX.1 Schnell — fast |
Image |
| Stable Diffusion 3 |
Video |
| MiniMax text-to-video |
Video |
| MiniMax image-to-video |
Video |
| HunyuanVideo |
Video |
| Mochi 1 |
Utility |
| Background removal |
License
MIT
Available Tools
3 toolsgenerate_imageA
Generate one or more images from a text prompt using Fal.ai image generation models. Supports models like FLUX (fal-ai/flux/dev, fal-ai/flux/schnell), Stable Diffusion, and more. Returns the URL(s) of the generated image(s).
Inputs: prompt (required), model_id, image_size, num_images, +2 more.
generate_videoA
Generate a video from a text prompt (text-to-video) or from an input image (image-to-video) using Fal.ai video generation models. Supports models like MiniMax (Hailuo AI), HunyuanVideo, Mochi 1, and more. Returns the URL of the generated video. Note: video generation can take 1–5 minutes.
Inputs: prompt (required), model_id, image_url, duration.
run_modelA
Run any Fal.ai model by its model ID with arbitrary input parameters. This is a flexible, low-level tool that gives you access to all 1000+ models on Fal.ai without needing a dedicated tool for each one. Consult the Fal.ai model catalog at https://fal.ai/models for available models and their parameters.
Inputs: model_id (required), input (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool targets a clearly distinct domain: image generation, video generation, and a catch-all for any other model. There is no ambiguity or overlap between them.
All tool names follow a consistent verb_noun pattern in snake_case (generate_image, generate_video, run_model), making them predictable and easy to understand.
Three tools is an ideal number for this server: two specialized tools for the most common tasks and one flexible tool that covers the remaining 1000+ models. The scope is well-scoped without being overwhelming.
The specialized tools handle the primary use cases (image and video generation), and run_model fills any gaps. However, dedicated tools for other popular model categories (e.g., audio) could improve discoverability, though run_model covers them.
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