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Image Generation MCP Server

by manascb1344
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JavaScript
Hybrid

图像生成 MCP 服务器

模型上下文协议 (MCP) 服务器，支持通过 Together AI 使用 Flux.1 Schnell 模型无缝生成高质量图像。该服务器提供标准化接口来指定图像生成参数。

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特征

  • 由 Flux.1 Schnell 模型提供支持的高质量图像生成

  • 支持自定义尺寸（宽度和高度）

  • 清除提示验证和 API 问题的错误处理

  • 轻松与 MCP 兼容客户端集成

  • 可选将图像以 PNG 格式保存到磁盘

Related MCP server: Image Generation MCP Server

安装

npm install together-mcp

或者直接运行：

npx together-mcp@latest

配置

添加到您的 MCP 服务器配置：

{
  "mcpServers": {
    "together-image-gen": {
      "command": "npx",
      "args": ["together-mcp@latest -y"],
      "env": {
        "TOGETHER_API_KEY": "<API KEY>"
      }
    }
  }
}

用法

服务器提供了一个工具： generate_image

使用generate_image

此工具只有一个必需参数 - 提示符。所有其他参数均为可选参数，如果未提供，则使用合理的默认值。

参数

{
  // Required
  prompt: string;          // Text description of the image to generate

  // Optional with defaults
  model?: string;          // Default: "black-forest-labs/FLUX.1-schnell-Free"
  width?: number;          // Default: 1024 (min: 128, max: 2048)
  height?: number;         // Default: 768 (min: 128, max: 2048)
  steps?: number;          // Default: 1 (min: 1, max: 100)
  n?: number;             // Default: 1 (max: 4)
  response_format?: string; // Default: "b64_json" (options: ["b64_json", "url"])
  image_path?: string;     // Optional: Path to save the generated image as PNG
}

最小请求示例

仅需要提示：

{
  "name": "generate_image",
  "arguments": {
    "prompt": "A serene mountain landscape at sunset"
  }
}

包含图像保存的完整请求示例

覆盖所有默认值并指定保存图像的路径：

{
  "name": "generate_image",
  "arguments": {
    "prompt": "A serene mountain landscape at sunset",
    "width": 1024,
    "height": 768,
    "steps": 20,
    "n": 1,
    "response_format": "b64_json",
    "model": "black-forest-labs/FLUX.1-schnell-Free",
    "image_path": "/path/to/save/image.png"
  }
}

响应格式

响应将是一个 JSON 对象，包含以下内容：

{
  "id": string,        // Generation ID
  "model": string,     // Model used
  "object": "list",
  "data": [
    {
      "timings": {
        "inference": number  // Time taken for inference
      },
      "index": number,      // Image index
      "b64_json": string    // Base64 encoded image data (if response_format is "b64_json")
      // OR
      "url": string        // URL to generated image (if response_format is "url")
    }
  ]
}

如果提供了 image_path 并且保存成功，则响应将包括保存位置的确认。

默认值

如果请求中未指定，则使用以下默认值：

  • 型号：“black-forest-labs/FLUX.1-schnell-Free”

  • 宽度：1024

  • 高度：768

  • 步骤：1

  • n：1

  • 响应格式：“b64_json”

重要说明

  1. 仅需要prompt参数

  2. 如果未提供，所有可选参数均使用默认值

  3. 提供时，参数必须满足其约束（例如宽度/高度范围）

  4. Base64 响应可能很大 - 对于较大的图像请使用 URL 格式

  5. 保存图像时，确保指定的目录存在且可写

先决条件

  • Node.js >= 16

  • Together AI API 密钥

    1. 登录api.together.xyz

    2. 导航至API 密钥设置

    3. 点击“创建”生成新的API密钥

    4. 复制生成的密钥以用于您的 MCP 配置

依赖项

{
  "@modelcontextprotocol/sdk": "0.6.0",
  "axios": "^1.6.7"
}

发展

克隆并构建项目：

git clone https://github.com/manascb1344/together-mcp-server
cd together-mcp-server
npm install
npm run build

可用脚本

  • npm run build构建 TypeScript 项目

  • npm run watch - 观察变化并重建

  • npm run inspector - 运行 MCP 检查器

贡献

欢迎贡献！请按以下步骤操作：

  1. 分叉存储库

  2. 创建新分支 ( feature/my-new-feature )

  3. 提交你的更改

  4. 将树枝推到你的叉子上

  5. 打开拉取请求

您可以通过 GitHub Issues 提交功能请求和错误报告。请先检查现有问题，然后再创建新问题。

对于重大更改，请先打开问题来讨论您提议的更改。

执照

本项目遵循 MIT 许可证。详情请参阅 LICENSE 文件。

Available Tools

1 tool
generate_imageC

Generate an image using Together AI API

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText prompt for image generation
modelNoModel to use for generation (default: black-forest-labs/FLUX.1-schnell-Free)
widthNoImage width (default: 1024)
heightNoImage height (default: 768)
stepsNoNumber of inference steps (default: 1)
nNoNumber of images to generate (default: 1)
response_formatNoResponse format (default: b64_json)
image_pathNoOptional path to save the generated image as PNG

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 mentions the API provider but fails to describe critical behaviors like rate limits, authentication requirements, cost implications, error handling, or what happens when saving to 'image_path'. This leaves significant gaps 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 extremely concise with a single sentence that directly states the tool's purpose. There is zero wasted language, and it's front-loaded with the core functionality, making it highly efficient.

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 (8 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain return values, error cases, or behavioral nuances, leaving the agent with incomplete information for proper tool invocation in a real-world context.

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 8 parameters. The description adds no additional parameter semantics beyond what's already in the schema, meeting the baseline score of 3 for high schema coverage without extra value.

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 action ('Generate an image') and the target resource ('using Together AI API'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a perfect score.

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, prerequisites, or context for invocation. It simply states what the tool does without any usage instructions or exclusions.

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.

  1. 1 tool updatev0.1.7
    • First observedgenerate_image
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TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making it impossible for an agent to misselect between multiple options.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.

Tool Count2/5

A single tool is too few for a server named 'Image Generation MCP Server', which suggests a broader scope. While the tool covers basic generation, the lack of additional tools (e.g., for editing, listing, or managing images) makes the surface feel thin and incomplete for the implied domain.

Completeness2/5

The server is severely incomplete for an image generation domain. It only provides a generate_image tool, with no coverage for related operations like listing generated images, editing parameters, deleting images, or handling variations. This creates significant gaps that will likely cause agent failures in broader workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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