FluxMCP
FluxMCP is an MCP server for AI image generation/editing and FLUX 3 video generation via AceDataCloud, plus task tracking and model reference tools.
Generate images from text prompts with multiple Flux models and configurable size, count, and callback URL.
Edit existing images using a prompt and image URL with Flux/Kontext/Flux-2 editing models.
Generate FLUX 3 videos in t2v, i2v, v2v, and draft-enhance modes with duration, resolution, aspect ratio, audio, async, and safety controls.
Query a single generation task or batch-query up to many task IDs for status and result URLs.
List available Flux models and tools/actions for model selection and workflow guidance.
Provides tools for AI image generation and editing using Flux models (flux-dev, flux-pro, flux-pro-1.1, flux-pro-1.1-ultra, flux-kontext-pro, and flux-kontext-max) via the AceDataCloud platform, enabling text-to-image generation and context-aware image modification.
FluxMCP
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 |
| Generate AI images from a text prompt using Flux. |
| Edit an existing image using Flux with a text prompt. |
| List all available Flux models and their capabilities. |
| List all available Flux tools and their use cases. |
| Generate FLUX 3 videos or enhance an owned draft. |
| Query the status and result of a Flux image generation task. |
| 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 |
Local stdio | The client runs a local MCP process | Install |
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, enterhttps://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 localclaude_desktop_config.jsonis a separate setup. Claude connector guide.Claude Code:
claude mcp add --transport http --scope user flux https://flux.mcp.acedata.cloud/mcp, thenclaude 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.jsonor the user profile. Check MCP: List Servers. VS Code MCP setup.Codex:
codex mcp add flux --url https://flux.mcp.acedata.cloud/mcp, thencodex 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
Open Ace Data Cloud → Applications. Sign in, or register an account first.
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.
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.
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.
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 HTTPAuthorizationheader needs theBearerprefix. Aplatform-...account-management token cannot replace a generation API key.


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-proFor 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
https://flux.mcp.acedata.cloud/healthreturning{"status":"ok"}checks endpoint reachability only.Confirm that the MCP client loads tools.
flux_list_modelsis a reference tool; it does not verify downstream API access or balance.If you need a full API check, call
flux_generate_imagewith your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, callflux_get_taskon 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 |
| Generate images from text prompts with model selection |
| Edit existing images with text instructions |
| Query status of a single generation task |
| Query multiple task statuses at once |
| List all available Flux models and capabilities |
| Show all tools and workflow examples |
Available Prompts
Prompt | Description |
| Guide for choosing the right tool and model |
| Best practices for writing effective prompts |
| Common workflow patterns and examples |
Supported Models
Model | Quality | Speed | Size Format | Best For |
| Good | Fast | Pixels (256-1440px) | Quick prototyping |
| High | Medium | Pixels (256-1440px) | Production use |
| High | Medium | Aspect ratios | Image editing |
| Highest | Slower | Aspect ratios | Complex editing |
| High | Fast | Aspect ratios | Flux 2 balanced quality |
| Higher | Medium | Aspect ratios | Flux 2 production |
| Highest | Slower | Aspect ratios | Flux 2 maximum quality |
| 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 |
| Yes (stdio) | — | API token from AceDataCloud |
| No |
| API base URL |
| No | — | OAuth client ID (hosted mode) |
| No |
| Platform base URL |
| No |
| Request timeout in seconds |
| No |
| MCP server name |
| No |
| 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 tokenLint & Format
ruff check .
ruff format .
mypy core tools main.pyTest
# Unit tests
pytest --cov=core --cov=tools
# Skip integration tests
pytest -m "not integration"
# With coverage report
pytest --cov=core --cov=tools --cov-report=htmlGit Hooks
git config core.hooksPath .githooksAPI 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
License
MIT License — see LICENSE for details.
Links
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 toolsflux_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
Scored across 7 tools
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.
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.
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.
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.
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