mcp-fal
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FAL_KEY | No | Your Fal.ai API key. Alternatively, provide via --api-key CLI argument. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_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). |
| 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. |
| 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.