openrouter-mcp-multimodal

MCP.Pizza Chef: stabgan

OpenRouter MCP Multimodal Server is an MCP server that integrates OpenRouter.ai's diverse model ecosystem to provide both text chat and image analysis capabilities. It supports simple text and multimodal conversations with configurable parameters, enabling rich interaction modes. The server facilitates direct access to OpenRouter chat models and powerful image analysis features, making it ideal for applications requiring combined text and visual data processing within the MCP framework.

Use This MCP server To

Enable multimodal chat combining text and images Perform image content analysis within chat workflows Configure chat model parameters like temperature Integrate OpenRouter chat models into MCP environments Analyze images for object detection and classification Support multimodal conversations in AI assistants Combine text and image data for richer context understanding

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"
            }
          }
        ]
      }
    ]
  }
});

openrouter-mcp-multimodal FAQ

How do I configure the temperature parameter for chat models?
The server allows configurable temperature settings to control response creativity and randomness in OpenRouter chat models.
Can this server handle both text-only and multimodal conversations?
Yes, it supports simple text chats as well as multimodal conversations involving images.
What image analysis capabilities does this server provide?
It offers image content analysis such as object detection and classification using OpenRouter's model ecosystem.
How do I integrate this MCP server with my existing MCP client?
You connect via standard MCP protocol endpoints, enabling seamless access to text and image analysis features.
Is this server compatible with other LLM providers besides OpenRouter?
While optimized for OpenRouter, it can be used alongside other providers like Claude and Gemini in MCP setups.
What deployment options are available?
The server is available via npm package and Docker image for flexible deployment.
How does this server enhance multimodal AI workflows?
By combining text chat and image analysis, it enables richer, context-aware AI interactions.