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jimeng-mcp-v4

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即梦 AI 图片生成 MCP 服务 V4.0

基于火山引擎即梦 AI 的图片生成 MCP（Model Context Protocol）服务。最新即梦生图 mcp servers, 支持最新的即梦 Seedream4.0 模型:jimeng_t2i_v40

功能特性

  • 使用火山引擎即梦 AI API 生成高质量图片

  • 支持多种图片比例：4:3、3:4、16:9、9:16

  • 标准化的 MCP 接口，兼容各种 MCP 客户端

  • 环境变量配置，安全便捷

Related MCP server: Doubao Image MCP Server

安装依赖

cd jimeng-mcp-v4
npm install

编译项目

npm run build

环境变量配置

设置以下环境变量：

export JIMENG_ACCESS_KEY="你的火山引擎AccessKey"
export JIMENG_SECRET_KEY="你的火山引擎SecretKey"

获取 API 密钥

  1. 访问 火山引擎控制台

  2. 登录后进入"即梦 AI"产品页面，开通服务（可选择免费试用）

  3. 在"访问控制"页面创建访问密钥，获取 Access Key 和 Secret Key

  4. 确保账号已开通即梦 AI 图像生成相关权限和策略

注意： 根据官方文档，请确保使用正确的 req_key 参数值 jimeng_high_aes_general_v21_L

使用方法

方式一: 使用 npx (推荐)

无需安装,直接在 MCP 客户端配置中使用:

配置文件位置:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

配置内容:

{
  "mcpServers": {
    "jimeng-mcp-v4": {
      "command": "npx",
      "args": ["-y", "jimeng-mcp-v4@latest"],
      "env": {
        "JIMENG_ACCESS_KEY": "你的AccessKey",
        "JIMENG_SECRET_KEY": "你的SecretKey"
      }
    }
  }
}

方式二: 全局安装

# 安装
npm install -g jimeng-mcp-v4

# 在配置文件中使用
{
  "mcpServers": {
    "jimeng-mcp-v4": {
      "command": "jimeng-mcp-v4",
      "env": {
        "JIMENG_ACCESS_KEY": "你的AccessKey",
        "JIMENG_SECRET_KEY": "你的SecretKey"
      }
    }
  }
}

方式三: 本地开发

# 克隆仓库
git clone https://github.com/yo4ai/jimeng-mcp-v4.git
cd jimeng-mcp-v4

# 安装依赖并构建
npm install
npm run build

# 在配置文件中使用
{
  "mcpServers": {
    "jimeng-mcp-v4": {
      "command": "node",
      "args": ["/path/to/jimeng-mcp-v4/build/index.js"],
      "env": {
        "JIMENG_ACCESS_KEY": "你的AccessKey",
        "JIMENG_SECRET_KEY": "你的SecretKey"
      }
    }
  }
}

API 接口

generate-image

当用户需要生成图片时使用的工具。

参数：

  • text (string): 用户需要在图片上显示的文字

  • illustration (string): 根据用户要显示的文字，提取 3-5 个可以作为图片配饰的插画元素关键词

  • color (string): 图片的背景主色调

  • ratio (enum): 图片比例，支持以下选项：

    • "4:3": 512×384

    • "3:4": 384×512

    • "16:9": 512×288

    • "9:16": 288×512

提示词生成规则： 工具会自动将输入参数组合成以下格式的提示词：

字体设计："{text}"，黑色字体，斜体，带阴影。干净的背景，白色到{color}渐变。点缀浅灰色、半透明{illustration}等元素插图做配饰插画。

返回：

  • 成功时返回图片 URL 和详细信息

  • 失败时返回错误信息

使用示例

// 在MCP客户端中调用
const result = await mcp.callTool('generate-image', {
  text: '新年快乐',
  illustration: '烟花, 灯笼, 祥云, 星星, 礼花',
  color: '红色',
  ratio: '4:3',
});

项目结构

jimeng-mcp-v4/
├── src/
│   └── index.ts          # 主服务文件
├── build/                # 编译输出目录
├── package.json          # 项目配置
├── tsconfig.json         # TypeScript配置
└── README.md            # 项目说明

注意事项

  1. 确保网络连接正常，能够访问火山引擎 API

  2. API 调用需要消耗积分，请注意使用量

  3. 生成的图片 URL 有时效性，建议及时下载保存

  4. 请遵守火山引擎的使用条款和即梦 AI 的内容政策

故障排除

常见错误

  1. 环境变量未设置：确保设置了正确的 ACCESS_KEY 和 SECRET_KEY

  2. 网络连接问题：检查网络连接和防火墙设置

  3. API 配额不足：检查火山引擎账户余额和 API 调用次数

  4. 提示词不合规：确保提示词符合内容安全规范

调试方法

运行时添加调试信息：

DEBUG=* node build/index.js

许可证

ISC License

Available Tools

1 tool
generate-imageB

当用户需要生成图片时使用的工具

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYes用于生成图像的提示词，中英文均可，最长不超过800字符。可以在prompt中描述图片内容、风格、尺寸比例等。API会智能判断生成2K分辨率图像。

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that the API generates 2K resolution images and sets a prompt length limit of 800 characters. However, it omits other behavioral traits like processing time, content safety filters, or cost implications.

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

Conciseness4/5

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

The description is a single concise sentence with no redundant information. It is front-loaded and efficient, though it could benefit from slightly more detail without losing conciseness.

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

Completeness3/5

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

For a simple generative tool with one parameter and no output schema, the description covers basic functionality and resolution. However, it lacks details on output format, error handling, and usage constraints, leaving gaps for an AI agent.

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 coverage is 100% with a detailed parameter description covering language, max length, content suggestions, and resolution hint. The tool description adds no additional meaning beyond the schema. Baseline 3 is appropriate given high coverage.

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 is used for generating images, which is a specific verb+resource pair. However, it lacks specificity about the type of generation (e.g., text-to-image, image-to-image). No sibling tools exist, so differentiation is not needed.

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 only implies usage when an image is needed. No explicit guidance on when to use this tool versus alternatives, prerequisites, or conditions to avoid. Without context or siblings, it provides minimal usage direction.

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 updatev4.0.0
    • First observedgenerate-image
Deploy Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.5/5.0

Scored across 1 tool

Disambiguation5/5

Only one tool exists, so there is no possibility of confusion or misselection.

Naming Consistency5/5

The single tool name 'generate-image' follows a clear verb_noun pattern, consistent with common MCP conventions.

Tool Count4/5

One tool is minimal but appropriate for a focused image generation server; however, the typical range for well-scoped servers is 3-15 tools.

Completeness3/5

The single tool covers the core generation task, but missing auxiliary operations like parameter preview or status checking may cause gaps for some workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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