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Jimeng MCP Server

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Jimeng MCP Server

A Model Context Protocol (MCP) server for Volcengine's Jimeng AI image generation service.

Features

  • Generate images using Volcengine's Jimeng AI service

  • Support for Chinese and English prompts

  • Configurable image dimensions and generation parameters

  • Watermark support

  • Built-in prompt enhancement (LLM preprocessing)

  • Super-resolution enhancement

Related MCP server: Jimeng MCP Server

Installation

npm install jimeng-mcp

Setup

  1. Get your Volcengine credentials from the Volcengine Console

  2. Set environment variables:

export VOLCENGINE_ACCESS_KEY_ID="your_access_key_id"
export VOLCENGINE_SECRET_ACCESS_KEY="your_secret_access_key"

Usage

With Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "jimeng": {
      "command": "npx",
      "args": ["jimeng-mcp"],
      "env": {
        "VOLCENGINE_ACCESS_KEY_ID": "your_access_key_id",
        "VOLCENGINE_SECRET_ACCESS_KEY": "your_secret_access_key"
      }
    }
  }
}

Standalone Usage

# Set environment variables
export VOLCENGINE_ACCESS_KEY_ID="your_access_key_id"
export VOLCENGINE_SECRET_ACCESS_KEY="your_secret_access_key"

# Run the server
npm start

Available Tools

generateImage

Generate images using text descriptions.

Parameters:

  • prompt (required): Text description for image generation

  • req_key (optional): Model identifier (default: "jimeng_high_aes_general_v21_L")

  • seed (optional): Random seed for generation (default: -1)

  • width (optional): Image width in pixels (default: 512)

  • height (optional): Image height in pixels (default: 512)

  • use_sr (optional): Enable super-resolution (default: true)

  • use_pre_llm (optional): Enable prompt enhancement (default: true)

  • return_url (optional): Return image URLs (default: true)

  • logo_info (optional): Watermark configuration

Example:

{
  "prompt": "一只可爱的小猫在花园里玩耍，阳光明媚，高清摄影",
  "width": 1024,
  "height": 1024,
  "use_sr": true
}

Development

# Install dependencies
npm install

# Build
npm run build

# Development mode
npm run dev

# Lint
npm run lint

# Format
npm run format

Configuration

Environment Variables

  • VOLCENGINE_ACCESS_KEY_ID: Your Volcengine access key ID

  • VOLCENGINE_SECRET_ACCESS_KEY: Your Volcengine secret access key

Watermark Configuration

{
  "logo_info": {
    "add_logo": true,
    "position": 0,     // 0: bottom-right, 1: bottom-left, 2: top-left, 3: top-right
    "language": 0,     // 0: Chinese, 1: English
    "logo_text_content": "AI Generated",
    "opacity": 0.3
  }
}

API Reference

This MCP server interfaces with Volcengine's Jimeng image generation API. For detailed API documentation, visit:

License

MIT

Contributing

  1. Fork the repository

  2. Create your feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add some amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. Open a Pull Request

Support

For issues and questions:

Security

  • Never commit API keys or secrets to version control

  • Use environment variables for sensitive configuration

  • Regularly rotate your API credentials

Available Tools

1 tool
generateImageC

调用即梦AI生成图像

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYes生成图像的文本描述
req_keyNo模型版本，默认值: jimeng_high_aes_general_v21_Ljimeng_high_aes_general_v21_L
seedNo随机种子，默认值：-1
negative_promptNo负面提示词，描述不希望在图像中出现的内容

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 the full burden. It mentions '即梦AI' but doesn't disclose behavioral traits like rate limits, authentication needs, output format, or error handling. This leaves significant gaps for an AI agent to understand tool 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 a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.

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 generation tool with no annotations and no output schema, the description is incomplete. It lacks details on output format, error cases, or usage constraints, making it inadequate for full contextual understanding.

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 provides. With 100% schema description coverage, the baseline is 3, as the schema adequately documents parameters like 'prompt' and 'req_key'. No additional value is added by the description.

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 '调用即梦AI生成图像' states the action (generate) and resource (image) but is vague about the specific AI service ('即梦AI') without further context. It doesn't distinguish from siblings as there are none, but the purpose is clear though not highly specific.

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?

No guidance on when to use this tool vs alternatives is provided. The description implies usage for image generation but lacks context on prerequisites, limitations, or scenarios. With no sibling tools, this is less critical, but still a gap in usage instructions.

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 update
    • First observedgenerateImage
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TDQS

C2.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or confusion between tools. The single tool 'generateImage' has a clearly defined purpose that cannot be mistaken for any other tool in the set.

Naming Consistency5/5

The naming pattern cannot be inconsistent with only one tool. The tool name 'generateImage' follows a clear verb_noun pattern, and there are no other tools to create any inconsistency.

Tool Count2/5

A single tool is generally too few for most server purposes, as it offers limited functionality and may not cover the domain adequately. While it might suffice for a very narrow scope, it feels thin and underdeveloped for typical MCP server applications.

Completeness2/5

Based on the server name 'Jimeng MCP Server' and the tool description '调用即梦AI生成图像' (calls Jimeng AI to generate images), the domain appears to be image generation. However, with only one tool, there are significant gaps—no tools for modifying, retrieving, or managing generated images, making the surface severely incomplete for practical workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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