fal-ai-mcp-server
An MCP server that gives Claude access to fal.ai generative AI models for images, videos, audio, and 3D content generation and editing.
README
fal.ai MCP Server
An MCP (Model Context Protocol) server that gives Claude access to fal.ai generative AI models — images, videos, audio, 3D models, and more.
Built with FastMCP. Works with Claude Code and Claude Desktop.
Model Catalog
Image Generation (10 models)
| Short Name | Model | Description | Price |
|---|---|---|---|
flux-dev |
FLUX.1 Dev | High-quality balanced generation | ~$0.025/image |
flux-schnell |
FLUX.1 Schnell | Fast generation (4 steps) | ~$0.003/image |
flux-pro |
FLUX.1 Pro v1.1 | Best quality FLUX model | ~$0.05/image |
flux-kontext |
FLUX Kontext Max | Context-aware editing, multi-image input | ~$0.08/image |
recraft-v4 |
Recraft V4 | Typography and design-oriented | ~$0.04/image |
nano-banana |
Nano Banana | Ultra-fast, ultra-cheap | ~$0.001/image |
nano-banana-2 |
Nano Banana 2 | Gemini 3.1 Flash — text rendering, consistency | ~$0.08/image |
nano-banana-pro |
Nano Banana Pro | Gemini 3 Pro — state-of-the-art | ~$0.15/image |
gpt-image |
GPT-Image 1.5 | OpenAI — high-fidelity, strong prompt adherence | ~$0.13/image |
gpt-image-2 |
GPT Image 2 | OpenAI — latest model, fine typography, detailed images | token-based, ~$0.10–0.30 |
seedream-v4 |
Seedream V4 | ByteDance — up to 4K resolution | ~$0.03/image |
Image Editing (4 models)
| Short Name | Model | Description | Price |
|---|---|---|---|
nano-banana-2-edit |
Nano Banana 2 Edit | Gemini 3.1 Flash — fast editing, text rendering | ~$0.08/edit |
nano-banana-pro-edit |
Nano Banana Pro Edit | Gemini 3 Pro — semantic editing, 14 ref images | ~$0.15/edit |
gpt-image-edit |
GPT-Image 1.5 Edit | OpenAI — preserves composition and lighting | ~$0.13/edit |
gpt-image-2-edit |
GPT Image 2 Edit | OpenAI — latest model, supports mask_url |
token-based, ~$0.10–0.30 |
seedream-v4-edit |
Seedream V4 Edit | ByteDance — context-aware editing | ~$0.03/edit |
Video Generation (5 models)
| Short Name | Model | Description | Price |
|---|---|---|---|
kling-v3-pro |
Kling V3 Pro | High-quality text/image to video | ~$0.30/video |
sora-2-t2v |
Sora 2 Text-to-Video | OpenAI Sora text-to-video | ~$0.50/video |
sora-2-i2v |
Sora 2 Image-to-Video | OpenAI Sora image-to-video | ~$0.50/video |
ltx-2 |
LTX Video V2 | Fast, affordable video | ~$0.05/video |
wan-v2 |
Wan 2.1 | Alibaba's video model | ~$0.10/video |
Audio Generation (4 models)
| Short Name | Model | Description | Price |
|---|---|---|---|
chatterbox-tts |
Chatterbox TTS | High-quality text-to-speech | ~$0.01/gen |
minimax-speech |
MiniMax Speech 02 HD | HD multi-voice synthesis | ~$0.02/gen |
beatoven-music |
Beatoven Music | AI music from text | ~$0.05/gen |
beatoven-sfx |
Beatoven SFX | Sound effects | ~$0.03/gen |
3D Generation (2 models)
| Short Name | Model | Description | Price |
|---|---|---|---|
meshy-v6 |
Meshy V6 | Image to 3D model | ~$0.15/model |
trellis-2 |
Trellis 2 | Fast image to 3D with textures | ~$0.10/model |
Utility (2 models)
| Short Name | Model | Description | Price |
|---|---|---|---|
remove-bg |
Background Removal | Remove image background | ~$0.005/image |
upscale |
Aura SR Upscale | 4x image upscaling | ~$0.01/image |
Available Tools
Synchronous (block until result, best for fast models)
| Tool | Description |
|---|---|
generate_image |
Generate images from text prompts |
edit_image |
Edit/transform existing images |
generate_video |
Generate video from text and/or image |
generate_audio |
Text-to-speech, music, and sound effects |
generate_3d |
Generate 3D models from images |
remove_background |
Remove image backgrounds |
upscale_image |
Upscale images to higher resolution |
run_model |
Run any fal.ai model with custom arguments |
list_models |
List all available models from the catalog |
Asynchronous queue API (for long-running jobs — video, 3D, large batches)
The synchronous tools block while the model runs. For models that take longer than the MCP tool-call timeout (~60 s), use the queue API instead: submit returns immediately with a request_id, then poll and fetch separately.
| Tool | Description |
|---|---|
submit_job(model, arguments, media_type, prefix, webhook_url?) |
Submit a job, return request_id immediately |
poll_job(request_id, with_logs?) |
Check status: IN_QUEUE (with queue_position), IN_PROGRESS (with logs), or COMPLETED |
fetch_job(request_id) |
Once COMPLETED, fetch result and download files locally |
cancel_job(request_id) |
Cancel a queued or in-progress job |
list_jobs(status_filter?, limit?) |
List locally tracked jobs (most recent first) |
Utilities
| Tool | Description |
|---|---|
read_file_base64(path) |
Read a local file and return {mime_type, size, data_url, base64}. Useful for inlining downloaded images into artifact UIs that can't load file:// due to CSP. |
Job metadata (request_id → model_id, media_type, prefix, status, files) is persisted in jobs.json next to server.py, so poll_job and fetch_job only need the request_id.
Example flow:
submit_job(model="kling-v3-pro", arguments={"prompt":"a fox jumping","duration":"5"}, media_type="video", prefix="fox")
→ {"request_id": "019dd...", "status": "SUBMITTED"}
poll_job("019dd...") → {"status": "IN_PROGRESS", ...}
poll_job("019dd...") → {"status": "COMPLETED"}
fetch_job("019dd...") → {"files": [{"local_path": "~/Downloads/fal-ai/videos/fox_..."}]}
Installation
Prerequisites
- Python 3.10+
- A fal.ai account with an API key
1. Get your fal.ai API key
Go to fal.ai Dashboard > Keys and create an API key.
2. Set up the server
git clone https://github.com/Szotasz/fal-ai-mcp-server.git
cd fal-ai-mcp-server
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
pip install -r requirements.txt
3. Set your API key
export FAL_KEY="your-api-key-here"
Usage with Claude Code
claude mcp add fal-ai \
-e FAL_KEY=your-api-key-here \
-- /path/to/fal-ai-mcp-server/.venv/bin/python /path/to/fal-ai-mcp-server/server.py
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"fal-ai": {
"command": "/path/to/fal-ai-mcp-server/.venv/bin/python",
"args": ["/path/to/fal-ai-mcp-server/server.py"],
"env": {
"FAL_KEY": "your-api-key-here"
}
}
}
}
Replace /path/to/fal-ai-mcp-server with the actual path where you cloned the repo.
Output
Generated files are automatically downloaded to ~/Downloads/fal-ai/ organized by type:
images/— generated and edited imagesvideos/— generated videosaudio/— speech, music, and sound effects3d/— 3D modelsother/— everything else
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.