# AceDataCloud/MCPFlux [Health: Active]

**Category:** 🎨 Art & Culture  
**Repository:** https://github.com/AceDataCloud/FluxMCP  
**GitHub Stars:** 3  
**Views:** 3  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/acedatacloud-mcpflux

## Description
Flux AI image generation and editing (Black Forest Labs) via Ace Data Cloud API.

## Tools
Capabilities this server exposes over MCP:

- **flux_generate_image** — Generate AI images from a text prompt using Flux.
- **flux_edit_image** — Edit an existing image using Flux with a text prompt.
- **flux_list_models** — List all available Flux models and their capabilities.
- **flux_list_actions** — List all available Flux tools and their use cases.
- **flux_get_task** — Query the status and result of a Flux image generation task.
- **flux_get_tasks_batch** — Query multiple Flux image generation tasks at once.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):

```json
"mcpServers": {
  "mcpflux": {
    "command": "uvx",
    "args": ["mcp-flux-pro"],
    "env": {
      "ACEDATACLOUD_API_TOKEN": ""
    }
  }
}
```

**Requires environment variables:** `ACEDATACLOUD_API_TOKEN` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What AceDataCloud/MCPFlux MCP server does

AceDataCloud/MCPFlux MCP server connects MCP clients to Flux models available through the Ace Data Cloud platform. It supports two primary image workflows: generating an image from a text prompt and editing an existing image with text instructions. The server also exposes model and action discovery tools, so clients can inspect supported Flux models and available workflows rather than relying only on fixed configuration.

The repository identifies support for models including `flux-dev`, `flux-pro`, and `flux-kontext`, with the README describing six Flux models for generation. Editing uses context-aware Flux Kontext models. Image requests are handled as asynchronous tasks, allowing a client to submit work and check its progress or result later.

## How it works

MCP-compatible clients call the server's tools. `flux_generate_image` accepts a text-based image request, while `flux_edit_image` applies a text instruction to an existing image. `flux_get_task` checks one task, and `flux_get_tasks_batch` checks multiple tasks in one request. `flux_list_models` returns model capabilities, and `flux_list_actions` describes available Flux actions and use cases.

AceDataCloud/MCPFlux MCP server supports local stdio transport and HTTP transport. The hosted endpoint is `https://flux.mcp.acedata.cloud/mcp`; hosted requests use a Bearer token. The local process can also run in HTTP mode, including inside a Docker container. HTTP mode supports per-request token handling, while the local stdio example supplies a token through the environment.

The project also provides MCP prompts for model and tool selection, prompt-writing guidance, and common workflow examples. These prompts are named `flux_image_generation_guide`, `flux_prompt_writing_guide`, and `flux_workflow_examples`.

## Setup and configuration

For the hosted service, create an account on the Ace Data Cloud platform, acquire an API token, and send it as a Bearer credential. Claude.ai is documented as a separate OAuth connection path that does not require entering the API token. Claude Desktop, Cursor, Windsurf, Cline, and other clients can connect to the hosted streamable HTTP URL through their MCP configuration.

For local use, install the `mcp-flux-pro` package from PyPI and set `ACEDATACLOUD_API_TOKEN`. The stdio process is started with `mcp-flux-pro`; the README also shows `uvx mcp-flux-pro`. HTTP mode can be started with `mcp-flux-pro --transport http --port 8000`. Docker users can run the published image `ghcr.io/acedatacloud/mcp-flux-pro:latest` and expose port 8000.

## Tools and capabilities

- Generate images from text prompts with model selection.
- Edit existing images using Flux text instructions.
- List Flux models and their capabilities.
- List available actions and workflow guidance.
- Query one asynchronous task or several tasks in a batch.
- Connect through local stdio, hosted HTTP, or self-hosted HTTP deployment.

## Limitations and notes

An Ace Data Cloud API token is required for the documented hosted and local API workflows, except for the Claude.ai OAuth connection described in the README. The supplied material does not specify API pricing, quotas, image retention, or detailed input and output schemas. The available model table is incomplete in the provided excerpt, so model-specific limits should be checked through `flux_list_models` or the provider documentation. AceDataCloud/MCPFlux MCP server is an interface to Flux through Ace Data Cloud; it does not describe a standalone image-generation model hosted in the repository.

_Full upstream README: https://allmcps.com/mcp/acedatacloud-mcpflux/readme_