
OpenRouter MCP Multimodal Server
MCP server for OpenRouter providing text chat and image analysis tools
What is OpenRouter MCP Multimodal Server?
OpenRouter MCP Multimodal Server is a server that provides text chat and image analysis capabilities through OpenRouter.ai's diverse model ecosystem, allowing users to engage in multimodal conversations and analyze images.
How to use OpenRouter MCP Multimodal Server?
To use the server, you can install it via npm or run it using Docker. After installation, configure it with your OpenRouter API key and choose a default model if desired.
Key features of OpenRouter MCP Multimodal Server?
- Text Chat: Access to all OpenRouter.ai chat models, supporting multimodal conversations with configurable parameters.
- Image Analysis: Analyze single or multiple images with custom questions, automatic resizing, and support for various image sources.
- Model Selection: Search, filter, and validate available models with detailed information.
- Performance Optimization: Smart caching, exponential backoff for retries, and automatic rate limit handling.
Use cases of OpenRouter MCP Multimodal Server?
- Engaging in complex conversations using text and images.
- Analyzing images for specific content or features.
- Utilizing various AI models for different tasks in a single interface.
FAQ from OpenRouter MCP Multimodal Server?
- Can I analyze multiple images at once?
Yes! The server supports analyzing multiple images simultaneously.
- Is there a limit to the number of models I can use?
No, you can search and filter through all available models in the OpenRouter ecosystem.
- How do I handle errors?
The server provides detailed error messages for various failure cases, including invalid input and network errors.
OpenRouter MCP Multimodal Server
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
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
- Get your OpenRouter API key from OpenRouter Keys
- 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",
"OPENROUTER_DEFAULT_MODEL": "anthropic/claude-3.5-sonnet"
}
}
}
}
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",
"OPENROUTER_DEFAULT_MODEL": "anthropic/claude-3.5-sonnet"
}
}
}
}
Option 3: Using Docker
{
"mcpServers": {
"openrouter": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e", "OPENROUTER_API_KEY=your-api-key-here",
"-e", "OPENROUTER_DEFAULT_MODEL=anthropic/claude-3.5-sonnet",
"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",
"OPENROUTER_DEFAULT_MODEL": "anthropic/claude-3.5-sonnet"
}
}
}
}
Available Tools
chat_completion
Send text or multimodal messages to OpenRouter models:
use_mcp_tool({
server_name: "openrouter",
tool_name: "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: "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"
}
}
]
}
]
}
});
multi_image_analysis
Analyze multiple images with a single prompt:
use_mcp_tool({
server_name: "openrouter",
tool_name: "multi_image_analysis",
arguments: {
images: [
{ url: "https://example.com/image1.jpg" },
{ url: "file:///absolute/path/to/image2.jpg" },
{
url: "https://example.com/image3.jpg",
alt: "Optional description of image 3"
}
],
prompt: "Compare these images and tell me their similarities and differences",
markdown_response: true, // Optional, defaults to true
model: "anthropic/claude-3-opus" // Optional if default is set
}
});
search_models
Search and filter available models:
use_mcp_tool({
server_name: "openrouter",
tool_name: "search_models",
arguments: {
query: "claude", // Optional text search
provider: "anthropic", // Optional provider filter
capabilities: {
vision: true // Filter for models with vision capabilities
},
limit: 5 // Optional, defaults to 10
}
});
get_model_info
Get detailed information about a specific model:
use_mcp_tool({
server_name: "openrouter",
tool_name: "get_model_info",
arguments: {
model: "anthropic/claude-3.5-sonnet"
}
});
validate_model
Check if a model ID is valid:
use_mcp_tool({
server_name: "openrouter",
tool_name: "validate_model",
arguments: {
model: "google/gemini-2.5-pro-exp-03-25:free"
}
});
Error Handling
The server provides detailed error messages for various failure cases:
- Invalid input parameters
- Network errors
- Rate limiting issues
- Invalid image formats
- Authentication problems
Troubleshooting
Common Issues
- "fetch is not defined" error: This often occurs when the Node.js environment doesn't have global fetch. Use Node.js v18+ or add the PATH environment variable to your configuration as shown below:
{
"mcpServers": {
"openrouter": {
"command": "npx",
"args": [
"-y",
"@stabgan/openrouter-mcp-multimodal"
],
"env": {
"OPENROUTER_API_KEY": "your-api-key-here",
"PATH": "/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
}
}
}
}
- Image analysis failures: Make sure your image path is absolute and the file format is supported.
Development
To build from source:
git clone https://github.com/stabgan/openrouter-mcp-multimodal.git
cd openrouter-mcp-multimodal
npm install
npm run build
License
MIT License
Version 1.2.0 Updates
- Simplified image analysis by consolidating all functionality into the
multi_image_analysis
tool - Added automatic selection of free models with the largest context window when no model is specified
- Improved handling of various image formats (file://, http://, data:)
- Enhanced error handling and logging for better troubleshooting
- Removed the
analyze_image
tool to eliminate confusion and streamline the interface