MCP Flux Studio
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FLUX_PATH | No | Path to Flux CLI installation (default: /Users/speed/CascadeProjects/flux) | /Users/speed/CascadeProjects/flux |
| BFL_API_KEY | Yes | Flux API key | |
| VIRTUAL_ENV | No | If set, uses $VIRTUAL_ENV/bin/python instead of python3 |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generateC | Generate an image from a text prompt |
| img2imgC | Generate an image using another image as reference |
| inpaintC | Inpaint an image using a mask |
| controlC | Generate an image using structural control |
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 4 tools
Each tool has a clearly distinct purpose with no overlap: 'control' uses structural guidance, 'generate' creates from text, 'img2img' references an image, and 'inpaint' modifies with a mask. The descriptions make it easy to differentiate between structural generation, text-to-image, image-to-image, and inpainting workflows.
The naming is mixed: 'control' and 'generate' are verbs only, while 'img2img' and 'inpaint' are compound terms. There's no consistent pattern like verb_noun, but the names are still readable and descriptive of their functions, avoiding chaotic conventions.
With 4 tools, this is well-scoped for an image generation server. Each tool earns its place by covering distinct aspects of image creation and manipulation, providing a focused set without being too thin or overwhelming for the domain.
The toolset covers core image generation workflows: text-to-image, image-to-image, inpainting, and controlled generation. A minor gap might be the lack of tools for post-processing or batch operations, but the essential CRUD-like operations for image creation are well-represented.