fal-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FAL_KEY | Yes | API key from fal.ai |
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 |
|---|---|
| tasks | {
"list": {},
"cancel": {},
"requests": {
"tools": {
"call": {}
},
"prompts": {
"get": {}
},
"resources": {
"read": {}
}
}
} |
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageA | Generate images from a text prompt using fal.ai. Common models:
Returns paths to saved images and metadata. |
| generate_with_referenceC | Generate images with a style/content reference image. Uses the reference_image feature of flux-general to guide generation toward a similar style or content as the reference. Args: reference_image_url: URL of the reference image for style guidance. reference_strength: How strongly to follow the reference (0.0-1.0). |
| generate_with_loraC | Generate images with a LoRA model applied. Args: lora_url: URL to the LoRA safetensors file (e.g. from HuggingFace). lora_scale: Strength of the LoRA effect (0.0-2.0, default 1.0). |
| edit_imageB | Edit an existing image using natural language instructions (FLUX Kontext). Pass a reference image and describe the changes you want in the prompt. Great for iterating on generated images. Args: image_url: URL of the image to edit. prompt: Description of the desired changes. |
| raw_generateA | Submit an arbitrary request to any fal.ai model endpoint. Use this for advanced configurations (ControlNet, IP-Adapter, multi-LoRA, custom parameters) that aren't covered by the other tools. Args: model: The fal.ai model endpoint ID (e.g. "fal-ai/flux-general"). body: The full JSON request body to send to the model. |
| list_outputsB | List all images previously saved in the output directory. |
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 6 tools
Most tools have distinct purposes: edit_image modifies existing images, generate_image creates from text, generate_with_lora adds LoRA models, generate_with_reference uses style guidance, list_outputs enumerates saved images, and raw_generate handles advanced configurations. However, generate_image and generate_with_lora could potentially overlap in functionality if LoRA is used for generation, but their descriptions clarify the distinction.
Tool names follow a consistent verb-based snake_case pattern (e.g., edit_image, generate_image, list_outputs), which is clear and predictable. The only minor deviation is raw_generate, which uses 'raw' as a prefix instead of a verb, but it still fits the overall naming style without causing confusion.
With 6 tools, the server is well-scoped for image generation and editing tasks. Each tool serves a specific role, from basic generation to advanced configurations, and the count is neither too sparse nor overwhelming, fitting typical MCP server ranges for this domain.
The toolset covers core image generation workflows: creation (generate_image), editing (edit_image), style control (generate_with_reference), model customization (generate_with_lora), and advanced options (raw_generate), plus management (list_outputs). A minor gap is the lack of a delete or manage tool for removing saved images, but agents can work around this.