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FluxMCP

PyPI version PyPI downloads CI License: MIT MCP Python 3.10+

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 8 Flux image 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

Related MCP server: DiffuGen

Tool Reference

Tool

Description

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_generate_video

Generate FLUX 3 videos or enhance an owned draft.

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.

Verify the connection before generating

After installation, discover the server's tools and call flux_list_models with no arguments. This reference tool returns locally defined model or action information without calling a generation API. It does not validate your application key, account balance, service access, or current price. Your client's Chat model can still create separate charges.

To check authenticated access without creating media, query a task already owned by your application using the task-lookup tool and the same original task ID. If you have no existing task, stop after discovery/reference output; do not generate media just to test installation. Review current pricing before any new generation.

For missing tools, reconnect and refresh the tool list. For 401/403, check the existing application's key, service permissions, Allowed APIs, expiration and usage limit before replacing credentials. Keep a task/trace ID for other failures, never your key; do not automatically retry a paid operation.

Connect: hosted OAuth, API token, or local stdio

The hosted endpoint is https://flux.mcp.acedata.cloud/mcp. Choose one route for the MCP client:

Route

When to use it

Credential setup

Hosted OAuth

The client supports remote MCP OAuth

Add only the URL, then sign in to AceDataCloud and approve access. No token needs to be pasted into client configuration.

Hosted API token

The client cannot finish OAuth, or you need an explicit integration credential

Send an AceDataCloud API token in the Authorization: Bearer … header. Keep it in a local secret store or environment variable.

Local stdio

The client runs a local MCP process

Install mcp-flux-pro and pass ACEDATACLOUD_API_TOKEN to that process. It still calls the AceDataCloud API.

The hosted service advertises OAuth metadata and Dynamic Client Registration (DCR). DCR registers the client application; it is not an API key. OAuth signs you in and the client sends the resulting Bearer token; it may reuse or create an API credential for the account. Browser sign-in still requires an AceDataCloud account. The hosted service can be metered: review current service documentation and displayed pricing before a real operation. Do not configure both an OAuth login and a fixed Authorization header for the same server.

Hosted OAuth examples

  • Claude and Claude Desktop chat: Add a remote custom connector in Customize → Connectors → Add custom connector, enter https://flux.mcp.acedata.cloud/mcp, select sign-in, and choose Register automatically if Claude asks how to register its OAuth client. Complete consent. Claude Desktop's local claude_desktop_config.json is a separate setup. Claude connector guide.

  • Claude Code: claude mcp add --transport http --scope user flux https://flux.mcp.acedata.cloud/mcp, then claude mcp login flux. Check /mcp. Claude Code MCP guide.

  • Cursor: Add a remote server with only https://flux.mcp.acedata.cloud/mcp. For a project, merge the entry below into <project>/.cursor/mcp.json; for personal use, use ~/.cursor/mcp.json. Cursor MCP guide.

  • VS Code / Copilot: Run MCP: Add Server, select HTTP, enter https://flux.mcp.acedata.cloud/mcp, then finish the browser sign-in. New portable workspace configs use <project>/.mcp.json; the VS Code-specific format below uses <project>/.vscode/mcp.json or the user profile. Check MCP: List Servers. VS Code MCP setup.

  • Codex: codex mcp add flux --url https://flux.mcp.acedata.cloud/mcp, then codex mcp login flux. Its user settings are in ~/.codex/config.toml. Official Codex MCP guide.

Cursor project config (OAuth):

{
  "mcpServers": {
    "flux": {"url": "https://flux.mcp.acedata.cloud/mcp"}
  }
}

VS Code-specific workspace config (OAuth):

{
  "servers": {
    "flux": {"type": "http", "url": "https://flux.mcp.acedata.cloud/mcp"}
  }
}

Hosted API token

  1. Open Ace Data Cloud → Applications. Sign in, or register an account first.

  2. Find General application. If it has not been created, choose Initialize General Application. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and current pricing.

  3. Copy the API Key from the application card, or open Keys / Manage Keys → Create to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.

  4. When creating a key, review Expiration Time, Set Usage Limitation, and Allowed APIs. Enable only the restrictions you need, allow the operations you will call, then click Create and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.

  5. Copy only the token value, without quotation marks, spaces, or Bearer . An API Key / API Token field expects this raw value; only a manually configured HTTP Authorization header needs the Bearer prefix. A platform-... account-management token cannot replace a generation API key.

Copy an application API key or open Manage Keys

Create a separately named key with optional restrictions

Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.

A fixed Bearer header bypasses OAuth. If the header is invalid, remove it before trying browser sign-in; keep the saved credential private.

For Claude Code, the shell expands the token when you add the server; treat the saved user MCP config as a secret:

export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
claude mcp add --transport http --scope user flux https://flux.mcp.acedata.cloud/mcp \
  --header "Authorization: Bearer $ACEDATACLOUD_API_TOKEN"

For a Claude Code project config, put a variable reference in <project>/.mcp.json and set that variable in the environment that launches Claude Code:

{
  "mcpServers": {
    "flux": {
      "type": "http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${ACEDATACLOUD_API_TOKEN}"}
    }
  }
}

Cursor uses a different environment-variable syntax in ~/.cursor/mcp.json or an uncommitted project config:

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

In VS Code, run MCP: Open User Configuration and merge this server plus its masked input; ${input:...} is for VS Code's user/workspace format and is not portable to the Agent Host .mcp.json format:

{
  "inputs": [
    {"id": "acedata-flux-token", "type": "promptString", "description": "AceDataCloud API token", "password": true}
  ],
  "servers": {
    "flux": {
      "type": "http",
      "url": "https://flux.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${input:acedata-flux-token}"}
    }
  }
}

For Cline, use its MCP configuration UI or CLI file ~/.cline/data/settings/cline_mcp_settings.json; its remote transport value is streamableHttp. For JetBrains AI Assistant, add a remote URL from Settings → Tools → AI Assistant → Model Context Protocol (MCP). For Zed, use a context_servers entry with the URL only for OAuth or add a local Bearer header. These clients have different configuration schemas; follow their current UI rather than copying another client's JSON. Cline · JetBrains · Zed.

Local stdio

Install the package and give the local process an API token:

python -m pip install mcp-flux-pro
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
mcp-flux-pro

For Claude Desktop local MCP, merge this entry into the file opened by its developer settings (~/Library/Application Support/Claude/claude_desktop_config.json on macOS). uvx requires uv on PATH:

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

Keep this user-level file private. Self-hosted HTTP uses mcp-flux-pro --transport http --port 8000; expose it only with suitable network and TLS controls. Local execution still calls the AceDataCloud API.

Check before using the service

  1. https://flux.mcp.acedata.cloud/health returning {"status":"ok"} checks endpoint reachability only.

  2. Confirm that the MCP client loads tools. flux_list_models is a reference tool; it does not verify downstream API access or balance.

  3. If you need a full API check, call flux_generate_image with your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, call flux_get_task on that same ID until terminal success or failure. Do not resubmit the operation just to check progress.

For 401, check which auth route the client used and whether the token or OAuth session is valid. A 403 may mean an account permission or content moderation failure; read the returned error. Insufficient balance and downstream service failures need their own diagnosis. A listed tool or submitted task does not prove a successful result.

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-kontext-pro

High

Medium

Aspect ratios

Image editing

flux-kontext-max

Highest

Slower

Aspect ratios

Complex editing

flux-2-flex

High

Fast

Aspect ratios

Flux 2 balanced quality

flux-2-pro

Higher

Medium

Aspect ratios

Flux 2 production

flux-2-max

Highest

Slower

Aspect ratios

Flux 2 maximum quality

flux-2-klein

Good

Fast

Aspect ratios

Flux 2 efficient output

Usage Examples

Generate an Image

"Generate a photorealistic mountain landscape at golden hour"
→ flux_generate_image(prompt="...", model="flux-2-max", size="16:9")

Edit an Image

"Add sunglasses to the person in this photo"
→ flux_edit_image(prompt="Add sunglasses", image_url="https://...", size="1:1", 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

No

—

OAuth client ID (hosted mode)

ACEDATACLOUD_PLATFORM_BASE_URL

No

https://platform.acedata.cloud

Platform base URL

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

Documentation

Documentation

License

MIT License — see LICENSE for details.

FLUX 3 video

Use flux_generate_video with a structured request for t2v, i2v, v2v or draft_enhance. For example: {"mode":"t2v","prompt":"Waves at sunset","duration":5,"resolution":"hd","generate_audio":false}. Image mode requires keyframes; video mode requires start_video. Draft enhancement uses an owned platform draft_task_id and requires the temporary draft cache still to be available.

Video generation uses POST /flux/videos with action=generate (default) and mode=t2v/i2v/v2v/draft_enhance. Each generation mode accepts its own fields. Tools return task IDs asynchronously by default; use flux_get_task for the final video. Set async=false in the request to wait synchronously.

Image model constraints

Use aspect ratios such as 1:1 or 16:9 for flux-2-flex, flux-2-pro, flux-2-max, and the Kontext models; pixel dimensions such as 1024x1024 are rejected for these models. flux-2-klein supports image generation only and is not an edit model.

Available Tools

7 tools
flux_edit_imageAInspect

Edit an existing image using Flux with a text prompt.

This allows you to modify an existing image based on a text description.
The kontext models (flux-kontext-pro, flux-kontext-max) are specifically
designed for high-quality image editing and style transfer.

Use this when:
- You want to modify or transform an existing image
- You want to change specific elements in an image
- You want to apply style changes or artistic effects
- You want to add, remove, or replace objects in an image

For generating new images from scratch, use flux_generate_image instead.

Returns:
    Task ID and edited image information including URLs.

Inputs: size (required), prompt (required), image_url (required), model, +1 more. Returns: result

flux_generate_imageAInspect

Generate AI images from a text prompt using Flux.

Flux is a family of fast, high-quality image generation models by Black Forest Labs.
Different models offer different tradeoffs between speed, quality, and capabilities.

Use this when:
- You want to create new images from a text description
- You need high-quality AI-generated artwork or photos
- You want fast image generation with good prompt following

For editing existing images, use flux_edit_image instead.

Returns:
    Task ID and generated image information including URLs.

Inputs: size (required), prompt (required), count, model, +1 more. Returns: result

flux_generate_videoBInspect

Generate text/image/video-to-video or enhance an owned temporary draft. Poll flux_get_task.

Inputs: request (required). Returns: result

flux_get_taskAInspect

Query the status and result of a Flux image or video generation task.

Use this to check if a generation is complete and retrieve the resulting
image/video URLs and metadata.

Use this when:
- You want to check if an image generation has completed
- You need to retrieve image URLs from a previous generation
- You used async callback and want to check results later
- The initial generation returned a task_id without immediate results

Returns:
    Task status and generation result including image URLs.

Inputs: task_id (required). Returns: result

flux_get_tasks_batchAInspect

Query multiple Flux image or video generation tasks at once.

Efficiently check the status of multiple tasks in a single request.
More efficient than calling flux_get_task multiple times.

Use this when:
- You have multiple pending generations to check
- You want to get status of several images at once
- You're tracking a batch of generations

Returns:
    Status and result information for all queried tasks.

Inputs: task_ids (required). Returns: result

flux_list_actionsAInspect

List all available Flux tools and their use cases.

Reference guide for what each tool does and when to use it.

Returns:
    Categorized list of all tools with descriptions.

Inputs: no named parameters. Returns: result

flux_list_modelsAInspect

List all available Flux models and their capabilities.

Reference guide for choosing the right Flux model for your use case.

Returns:
    Detailed list of all Flux models with descriptions and recommendations.

Inputs: no named parameters. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4/5.0

    Scored across 7 tools

    Disambiguation4/5

    The core tools are clearly separated: generate vs edit vs video generation, and single-task vs batch task queries are explicitly distinguished. Minor overlap exists because flux_generate_video bundles several video workflows and flux_get_tasks_batch could be seen as an optimization of flux_get_task, but descriptions make the intended boundaries clear.

    Naming Consistency5/5

    All tools follow a consistent flux_verb_noun snake_case pattern, such as flux_generate_image, flux_edit_image, flux_get_task, and flux_list_models. The convention is predictable throughout.

    Tool Count5/5

    Seven tools is well-scoped for an image/video generation service. The set covers generation, editing, task polling, batch polling, and reference discovery without obvious padding.

    Completeness4/5

    The surface covers the main lifecycle: generate/edit media, generate video, poll tasks, batch poll, and discover models/actions. Minor gaps remain around cancellation or listing existing tasks, but core workflows appear supported.

    Maintenance

    ActivityActive
    ResponsivenessLimited data

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