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MCP Flux Studio

Name: MCP Flux Studio
Author: jmanhype

by jmanhype

Overview Schema Source Related Servers Discussions Admin

JavaScript

Remote

MCP Flux 工作室

铁匠徽章

强大的模型上下文协议 (MCP) 服务器，将 Flux 的高级图像生成功能带入您的 AI 编码助手。该服务器支持将 Flux 的图像生成、操作和控制功能直接集成到 Cursor 和 Windsurf (Codeium) IDE 中。

概述

MCP Flux Studio 弥合了 AI 编码助手和 Flux 强大的图像生成 API 之间的差距，允许将图像生成功能无缝集成到您的开发工作流程中。

特征

图像生成
- 精确控制的文本到图像生成
- 多模型支持（flux.1.1-pro、flux.1-pro、flux.1-dev、flux.1.1-ultra）
- 可定制的宽高比和尺寸
图像处理
- 图像到图像的转换
- 使用可自定义的蒙版进行修复
- 分辨率提升和增强
高级控制
- 基于边缘的生成（精明）
- 深度感知生成
- 姿势引导生成
IDE 集成
- 完全支持 Cursor (v0.45.7+)
- 与 Windsurf/Codeium Cascade (Wave 3+) 兼容
- 通过人工智能助手无缝调用工具

Related MCP server: Flux Schnell MCP Server

快速入门

先决条件
- Node.js 18+
- Python 3.12+
- Flux API 密钥
- 兼容 IDE（Cursor 或 Windsurf）
安装

通过 Smithery 安装

要通过Smithery自动安装 Flux Studio for Claude Desktop：

npx -y @smithery/cli install @jmanhype/mcp-flux-studio --client claude

手动安装

git clone https://github.com/jmanhype/mcp-flux-studio.git
cd mcp-flux-studio
npm install
npm run build

基本配置

BFL_API_KEY=your_flux_api_key
FLUX_PATH=/path/to/flux/installation

有关详细的设置说明（包括特定于 IDE 的配置和故障排除），请参阅我们的安装指南。

文档

安装指南- 全面的安装说明
API 文档- 详细的工具文档
示例用法- 真实使用示例
贡献指南- 如何贡献

IDE 集成

光标（v0.45.7+）

MCP Flux Studio 与 Cursor 的 AI 助手无缝集成：

配置
- 通过“设置”>“功能”>“MCP”进行配置
- 支持 stdio 和 SSE 连接
- 可以通过包装脚本设置环境变量
用法
- Cursor 的 AI 助手自动可用的工具
- 工具调用需要用户批准
- 实时反馈生成进度

风帆冲浪/Codeium（Wave 3+）

与 Windsurf 的 Cascade AI 集成：

配置
- 编辑~/.codeium/windsurf/mcp_config.json
- 支持基于流程的工具执行
- JSON 格式配置的环境变量
用法
- 通过 Cascade 的 MCP 工具栏访问工具
- 自动工具发现和加载
- 与 Cascade 的 AI 功能集成

有关 IDE 特定设置的详细信息，请参阅安装指南。

用法

该服务器提供以下工具：

产生

根据文本提示生成图像。

{
  "prompt": "A photorealistic cat",
  "model": "flux.1.1-pro",
  "aspect_ratio": "1:1",
  "output": "generated.jpg"
}

img2img

使用另一幅图像作为参考来生成一幅图像。

{
  "image": "input.jpg",
  "prompt": "Convert to oil painting",
  "model": "flux.1.1-pro",
  "strength": 0.85,
  "output": "output.jpg",
  "name": "oil_painting"
}

修复

使用蒙版修复图像。

{
  "image": "input.jpg",
  "prompt": "Add flowers",
  "mask_shape": "circle",
  "position": "center",
  "output": "inpainted.jpg"
}

控制

使用结构控制生成图像。

{
  "type": "canny",
  "image": "control.jpg",
  "prompt": "A realistic photo",
  "output": "controlled.jpg"
}

发展

项目结构

flux-mcp-server/
├── src/
│   ├── index.ts          # Main server implementation
│   └── types.ts          # TypeScript type definitions
├── tests/
│   └── server.test.ts    # Server tests
├── docs/
│   ├── API.md           # API documentation
│   └── CONTRIBUTING.md  # Contribution guidelines
├── examples/
│   ├── generate.json    # Example tool usage
│   └── config.json      # Example configuration
├── package.json
├── tsconfig.json
└── README.md

运行测试

npm test

建筑

npm run build

贡献

请阅读CONTRIBUTING.md了解我们的行为准则和提交拉取请求的流程的详细信息。

执照

该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅LICENSE文件。

致谢

模型上下文协议- 协议规范
Flux API——底层图像生成 API

Available Tools

4 tools

controlC

Generate an image using structural control

ParametersJSON Schema

Name	Required	Description
`type`	Yes	Type of control to use
`image`	Yes	Input control image path
`prompt`	Yes	Text prompt for generation
`steps`	No	Number of inference steps
`guidance`	No	Guidance scale
`output`	No	Output filename

TDQS

C2.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'structural control' but doesn't explain what this entails operationally—such as how control affects generation, whether it modifies existing images or creates new ones, potential side effects, or performance characteristics. This leaves significant gaps for a tool with 6 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'structural control' means, how it interacts with parameters, or what the tool returns. For a generation tool with multiple controls, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds no additional meaning about parameters beyond implying 'structural control' relates to the 'type' parameter. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Generate an image using structural control' states a clear purpose (generating images with control mechanisms) but is vague about what 'structural control' means and doesn't distinguish from sibling tools like 'generate', 'img2img', or 'inpaint'. It doesn't specify what makes this tool unique compared to those alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus the sibling tools ('generate', 'img2img', 'inpaint'). There's no mention of appropriate contexts, prerequisites, or exclusions. The agent must infer usage from the tool name and parameters alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generateC

Generate an image from a text prompt

ParametersJSON Schema

Name	Required	Description	Default
`prompt`	Yes	Text prompt for image generation
`model`	No	Model to use for generation	flux.1.1-pro
`aspect_ratio`	No	Aspect ratio of the output image
`width`	No	Image width (ignored if aspect-ratio is set)
`height`	No	Image height (ignored if aspect-ratio is set)
`output`	No	Output filename	generated.jpg

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It mentions generation but doesn't cover critical aspects like whether this is a read-only or destructive operation, potential rate limits, authentication needs, or what the output entails (e.g., image format, storage location). This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded with a single, clear sentence that directly states the tool's core function. There is no wasted language or redundancy, making it efficient and easy to parse, though this brevity contributes to gaps in other dimensions like guidelines and transparency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a 6-parameter image generation tool with no annotations and no output schema, the description is incomplete. It fails to address behavioral traits, usage context, or output details (e.g., what is returned, error handling), leaving the agent under-informed for effective tool invocation in a real-world scenario.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds no parameter semantics beyond what the input schema already provides, as schema description coverage is 100%. The schema thoroughly documents all 6 parameters, including enums for 'model' and 'aspect_ratio', defaults, and dependencies (e.g., 'width'/'height' ignored if 'aspect-ratio' set). Thus, the description meets the baseline but doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('generate') and resource ('image from a text prompt'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'img2img' or 'inpaint' which likely also generate images but from different inputs, leaving room for potential confusion about when to choose this specific tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'control', 'img2img', or 'inpaint'. It lacks context about prerequisites, such as needing a text prompt as input, or exclusions, like not being suitable for image-to-image transformations. This absence leaves the agent without clear direction for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

img2imgC

Generate an image using another image as reference

ParametersJSON Schema

Name	Required	Description	Default
`image`	Yes	Input image path
`prompt`	Yes	Text prompt for generation
`model`	No	Model to use for generation	flux.1.1-pro
`strength`	No	Generation strength
`width`	No	Output image width
`height`	No	Output image height
`output`	No	Output filename	outputs/generated.jpg
`name`	Yes	Name for the generation

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states the tool generates an image but doesn't disclose behavioral traits such as whether it overwrites files, requires specific permissions, has rate limits, or what the output format/behavior is (e.g., file creation, error handling). This is a significant gap for a tool with 8 parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for the tool's complexity, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 parameters, no annotations, no output schema), the description is insufficient. It doesn't explain the tool's behavior, output (e.g., file saved to disk), or usage context relative to siblings. For an image generation tool with multiple parameters, more detail is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all parameters well (e.g., 'image' as input path, 'prompt' for text, 'strength' for generation intensity). The description adds no additional meaning beyond implying the 'image' parameter is used as a reference, which is somewhat redundant with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate an image using another image as reference.' It specifies both the action ('generate') and the resource ('image'), though it doesn't explicitly differentiate from sibling tools like 'generate' or 'inpaint' beyond the reference image aspect.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'generate' (which likely generates from text only) or 'inpaint' (which might modify parts of an image). It mentions using an image as reference but doesn't clarify scenarios or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

inpaintC

Inpaint an image using a mask

ParametersJSON Schema

Name	Required	Description	Default
`image`	Yes	Input image path
`prompt`	Yes	Text prompt for inpainting
`mask_shape`	No	Shape of the mask	circle
`position`	No	Position of the mask	center
`output`	No	Output filename	inpainted.jpg

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('inpaint') but doesn't explain what inpainting entails (e.g., filling masked areas based on a prompt), potential side effects, permissions needed, or output behavior. This leaves significant gaps for a tool that modifies images.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste—'Inpaint an image using a mask'—making it highly concise and front-loaded. Every word earns its place by conveying the core action and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of an image inpainting tool with no annotations and no output schema, the description is insufficient. It doesn't explain what inpainting does, how the output is handled, or any behavioral traits, leaving the agent with incomplete context for proper tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (image, prompt, mask_shape, position, output) with descriptions and enums. The description adds no additional meaning beyond what the schema provides, such as explaining how the prompt influences inpainting or how mask shape/position interact. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Inpaint an image using a mask' clearly states the action (inpaint) and resource (image with mask), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'img2img' or 'generate', which might also involve image manipulation, so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'img2img' or 'generate'. It lacks context about specific use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

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

4 tool updatesAug 18, 2025
- First observedcontrol
- First observedgenerate
- First observedimg2img
- First observedinpaint

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored 2026-04-18 11:35 across 4 tools

Disambiguation5/5

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.

Naming Consistency3/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Reddit Discussion

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MCP Flux 工作室

概述

特征

快速入门

通过 Smithery 安装

手动安装

文档

IDE 集成

光标（v0.45.7+）

风帆冲浪/Codeium（Wave 3+）

用法

产生

img2img

修复

控制

发展

项目结构

运行测试

建筑

贡献

执照

致谢

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches