MCP Flux

<!-- mcp-name: io.github.AceDataCloud/mcp-flux-pro -->

A Model Context Protocol (MCP) server for AI image generation and editing using Flux through the AceDataCloud platform.

Generate and edit stunning AI images with Flux models (flux-dev, flux-pro, flux-kontext) directly from Claude, Cursor, or any MCP-compatible client.

Features

• 🎨 Image Generation — Generate images from text prompts with 6 Flux models
• ✏️ Image Editing — Edit existing images with context-aware Flux Kontext models
• 🔄 Task Management — Track async generation tasks and batch status queries
• 📋 Model Guide — Built-in model selection and prompt writing guidance
• 🌐 Dual Transport — stdio (local) and HTTP (remote/cloud) modes
• 🐳 Docker Ready — Containerized with K8s deployment manifests
• 🔒 Secure — Bearer token auth with per-request isolation in HTTP mode

Quick Start

1. Get Your API Token

1. Sign up at AceDataCloud Platform
2. Go to the API documentation page
3. Click "Acquire" to get your API token
4. Copy the token for use below

2. Use the Hosted Server (Recommended)

AceDataCloud hosts a managed MCP server — no local installation required.

Endpoint: https://flux.mcp.acedata.cloud/mcp

All requests require a Bearer token. Use the API token from Step 1.

Claude.ai

Connect directly on Claude.ai with OAuth — no API token needed:

1. Go to Claude.ai Settings → Integrations → Add More
2. Enter the server URL: https://flux.mcp.acedata.cloud/mcp
3. Complete the OAuth login flow
4. Start using the tools in your conversation

Claude Desktop

Add to your config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "flux": {
      "type": "streamable-http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cursor / Windsurf

Add to your MCP config (.cursor/mcp.json or .windsurf/mcp.json):

{
  "mcpServers": {
    "flux": {
      "type": "streamable-http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

VS Code (Copilot)

Add to your VS Code MCP config (.vscode/mcp.json):

{
  "servers": {
    "flux": {
      "type": "streamable-http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Or install the Ace Data Cloud MCP extension for VS Code, which bundles all 11 MCP servers with one-click setup.

JetBrains IDEs

1. Go to Settings → Tools → AI Assistant → Model Context Protocol (MCP)
2. Click Add → HTTP
3. Paste:

{
  "mcpServers": {
    "flux": {
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Claude Code

Claude Code supports MCP servers natively:

claude mcp add flux --transport http https://flux.mcp.acedata.cloud/mcp \
  -h "Authorization: Bearer YOUR_API_TOKEN"

Or add to your project's .mcp.json:

{
  "mcpServers": {
    "flux": {
      "type": "streamable-http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cline

Add to Cline's MCP settings (.cline/mcp_settings.json):

{
  "mcpServers": {
    "flux": {
      "type": "streamable-http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Amazon Q Developer

Add to your MCP configuration:

{
  "mcpServers": {
    "flux": {
      "type": "streamable-http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Roo Code

Add to Roo Code MCP settings:

{
  "mcpServers": {
    "flux": {
      "type": "streamable-http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Continue.dev

Add to .continue/config.yaml:

mcpServers:
  - name: flux
    type: streamable-http
    url: https://flux.mcp.acedata.cloud/mcp
    headers:
      Authorization: "Bearer YOUR_API_TOKEN"

Zed

Add to Zed's settings (~/.config/zed/settings.json):

{
  "language_models": {
    "mcp_servers": {
      "flux": {
        "url": "https://flux.mcp.acedata.cloud/mcp",
        "headers": {
          "Authorization": "Bearer YOUR_API_TOKEN"
        }
      }
    }
  }
}

cURL Test

# Health check (no auth required)
curl https://flux.mcp.acedata.cloud/health

# MCP initialize
curl -X POST https://flux.mcp.acedata.cloud/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

3. Or Run Locally (Alternative)

If you prefer to run the server on your own machine:

# Install from PyPI
pip install mcp-flux-pro
# or
uvx mcp-flux-pro

# Set your API token
export ACEDATACLOUD_API_TOKEN="your_token_here"

# Run (stdio mode for Claude Desktop / local clients)
mcp-flux-pro

# Run (HTTP mode for remote access)
mcp-flux-pro --transport http --port 8000

Claude Desktop (Local)

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

Docker (Self-Hosting)

docker pull ghcr.io/acedatacloud/mcp-flux-pro:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-flux-pro:latest

Clients connect with their own Bearer token — the server extracts the token from each request's Authorization header.

Cursor Integration

Add to your Cursor MCP configuration (.cursor/mcp.json):

{
  "mcpServers": {
    "flux": {
      "command": "mcp-flux-pro",
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

JetBrains IDEs

Install the Flux MCP plugin from the JetBrains Marketplace, or configure manually:

1. Go to Settings → Tools → AI Assistant → Model Context Protocol (MCP)
2. Click Add and select HTTP
3. Paste this configuration:

{
  "mcpServers": {
    "flux": {
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer your_api_token_here"
      }
    }
  }
}

Remote HTTP Mode

For cloud deployment or shared servers:

mcp-flux-pro --transport http --port 8000

Connect from clients using the HTTP endpoint:

{
  "mcpServers": {
    "flux": {
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer your_api_token_here"
      }
    }
  }
}

Docker

# Build
docker build -t mcp-flux .

# Run
docker run -p 8000:8000 mcp-flux

Or using Docker Compose:

docker compose up --build

Available Tools

Tool	Description
flux_generate_image	Generate images from text prompts with model selection
flux_edit_image	Edit existing images with text instructions
flux_get_task	Query status of a single generation task
flux_get_tasks_batch	Query multiple task statuses at once
flux_list_models	List all available Flux models and capabilities
flux_list_actions	Show all tools and workflow examples

Available Prompts

Prompt	Description
flux_image_generation_guide	Guide for choosing the right tool and model
flux_prompt_writing_guide	Best practices for writing effective prompts
flux_workflow_examples	Common workflow patterns and examples

Supported Models

Model	Quality	Speed	Size Format	Best For
flux-dev	Good	Fast	Pixels (256-1440px)	Quick prototyping
flux-pro	High	Medium	Pixels (256-1440px)	Production use
flux-pro-1.1	High	Medium	Pixels (256-1440px)	Better prompt following
flux-pro-1.1-ultra	Highest	Slower	Aspect ratios	Maximum quality
flux-kontext-pro	High	Medium	Aspect ratios	Image editing
flux-kontext-max	Highest	Slower	Aspect ratios	Complex editing

Usage Examples

Generate an Image

"Generate a photorealistic mountain landscape at golden hour"
→ flux_generate_image(prompt="...", model="flux-pro-1.1-ultra", size="16:9")

Edit an Image

"Add sunglasses to the person in this photo"
→ flux_edit_image(prompt="Add sunglasses", image_url="https://...", model="flux-kontext-pro")

Check Task Status

"What's the status of my generation?"
→ flux_get_task(task_id="...")

Environment Variables

Variable	Required	Default	Description
ACEDATACLOUD_API_TOKEN	Yes (stdio)	—	API token from AceDataCloud
ACEDATACLOUD_API_BASE_URL	No	https://api.acedata.cloud	API base URL
ACEDATACLOUD_OAUTH_CLIENT_ID	OAuth client ID (hosted mode)	—
ACEDATACLOUD_PLATFORM_BASE_URL	Platform base URL	https://platform.acedata.cloud
FLUX_REQUEST_TIMEOUT	No	1800	Request timeout in seconds
MCP_SERVER_NAME	No	flux	MCP server name
LOG_LEVEL	No	INFO	Logging level

Development

Setup

git clone https://github.com/AceDataCloud/FluxMCP.git
cd FluxMCP
pip install -e ".[all]"
cp .env.example .env
# Edit .env with your API token

Lint & Format

ruff check .
ruff format .
mypy core tools main.py

Test

# Unit tests
pytest --cov=core --cov=tools

# Skip integration tests
pytest -m "not integration"

# With coverage report
pytest --cov=core --cov=tools --cov-report=html

Git Hooks

git config core.hooksPath .githooks

API Reference

This MCP server uses the AceDataCloud Flux API:

• POST /flux/images — Generate or edit images
• POST /flux/tasks — Query task status (single or batch)

Full API documentation: platform.acedata.cloud

License

MIT License — see LICENSE for details.

Links

• AceDataCloud Platform
• MCP Protocol
• Flux by Black Forest Labs
• PyPI Package