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OpenRouter 多模态分析工具

通过OpenRouter.ai多样化的模型生态系统提供聊天和图像分析功能,支持文本对话以及使用各种AI模型进行强大的多模态图像处理。

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README

OpenRouter MCP Multimodal Server

Build Status npm version Docker Pulls

An MCP (Model Context Protocol) server that provides chat and image analysis capabilities through OpenRouter.ai's diverse model ecosystem. This server combines text chat functionality with powerful image analysis capabilities.

Features

  • Text Chat:

    • Direct access to all OpenRouter.ai chat models
    • Support for simple text and multimodal conversations
    • Configurable temperature and other parameters
  • Image Analysis:

    • Analyze single images with custom questions
    • Process multiple images simultaneously
    • Automatic image resizing and optimization
    • Support for various image sources (local files, URLs, data URLs)
  • Model Selection:

    • Search and filter available models
    • Validate model IDs
    • Get detailed model information
    • Support for default model configuration
  • Performance Optimization:

    • Smart model information caching
    • Exponential backoff for retries
    • Automatic rate limit handling

What's New in 1.5.0

  • Improved OS Compatibility:

    • Enhanced path handling for Windows, macOS, and Linux
    • Better support for Windows-style paths with drive letters
    • Normalized path processing for consistent behavior across platforms
  • MCP Configuration Support:

    • Cursor MCP integration without requiring environment variables
    • Direct configuration via MCP parameters
    • Flexible API key and model specification options
  • Robust Error Handling:

    • Improved fallback mechanisms for image processing
    • Better error reporting with specific diagnostics
    • Multiple backup strategies for file reading
  • Image Processing Enhancements:

    • More reliable base64 encoding for all image types
    • Fallback options when Sharp module is unavailable
    • Better handling of large images with automatic optimization

Installation

Option 1: Install via npm

npm install -g @stabgan/openrouter-mcp-multimodal

Option 2: Run via Docker

docker run -i -e OPENROUTER_API_KEY=your-api-key-here stabgandocker/openrouter-mcp-multimodal:latest

Quick Start Configuration

Prerequisites

  1. Get your OpenRouter API key from OpenRouter Keys
  2. Choose a default model (optional)

MCP Configuration Options

Add one of the following configurations to your MCP settings file (e.g., cline_mcp_settings.json or claude_desktop_config.json):

Option 1: Using npx (Node.js)

{
  "mcpServers": {
    "openrouter": {
      "command": "npx",
      "args": [
        "-y",
        "@stabgan/openrouter-mcp-multimodal"
      ],
      "env": {
        "OPENROUTER_API_KEY": "your-api-key-here",
        "DEFAULT_MODEL": "qwen/qwen2.5-vl-32b-instruct:free"
      }
    }
  }
}

Option 2: Using uv (Python Package Manager)

{
  "mcpServers": {
    "openrouter": {
      "command": "uv",
      "args": [
        "run",
        "-m",
        "openrouter_mcp_multimodal"
      ],
      "env": {
        "OPENROUTER_API_KEY": "your-api-key-here",
        "DEFAULT_MODEL": "qwen/qwen2.5-vl-32b-instruct:free"
      }
    }
  }
}

Option 3: Using Docker

{
  "mcpServers": {
    "openrouter": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e", "OPENROUTER_API_KEY=your-api-key-here",
        "-e", "DEFAULT_MODEL=qwen/qwen2.5-vl-32b-instruct:free",
        "stabgandocker/openrouter-mcp-multimodal:latest"
      ]
    }
  }
}

Option 4: Using Smithery (recommended)

{
  "mcpServers": {
    "openrouter": {
      "command": "smithery",
      "args": [
        "run",
        "stabgan/openrouter-mcp-multimodal"
      ],
      "env": {
        "OPENROUTER_API_KEY": "your-api-key-here",
        "DEFAULT_MODEL": "qwen/qwen2.5-vl-32b-instruct:free"
      }
    }
  }
}

Examples

For comprehensive examples of how to use this MCP server, check out the examples directory. We provide:

  • JavaScript examples for Node.js applications
  • Python examples with interactive chat capabilities
  • Code snippets for integrating with various applications

Each example comes with clear documentation and step-by-step instructions.

Dependencies

This project uses the following key dependencies:

  • @modelcontextprotocol/sdk: ^1.8.0 - Latest MCP SDK for tool implementation
  • openai: ^4.89.1 - OpenAI-compatible API client for OpenRouter
  • sharp: ^0.33.5 - Fast image processing library
  • axios: ^1.8.4 - HTTP client for API requests
  • node-fetch: ^3.3.2 - Modern fetch implementation

Node.js 18 or later is required. All dependencies are regularly updated to ensure compatibility and security.

Available Tools

mcp_openrouter_chat_completion

Send text or multimodal messages to OpenRouter models:

use_mcp_tool({
  server_name: "openrouter",
  tool_name: "mcp_openrouter_chat_completion",
  arguments: {
    model: "google/gemini-2.5-pro-exp-03-25:free", // Optional if default is set
    messages: [
      {
        role: "system",
        content: "You are a helpful assistant."
      },
      {
        role: "user",
        content: "What is the capital of France?"
      }
    ],
    temperature: 0.7 // Optional, defaults to 1.0
  }
});

For multimodal messages with images:

use_mcp_tool({
  server_name: "openrouter",
  tool_name: "mcp_openrouter_chat_completion",
  arguments: {
    model: "anthropic/claude-3.5-sonnet",
    messages: [
      {
        role: "user",
        content: [
          {
            type: "text",
            text: "What's in this image?"
          },
          {
            type: "image_url",
            image_url: {
              url: "https://example.com/image.jpg"
            }
          }
        ]
      }
    ]
  }
});
help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端