by stabgan
OpenRouter offers seamless access to diverse AI models for multimodal vision and language, with smart model selection an
Connects to OpenRouter.ai's AI model ecosystem for text chat and image analysis capabilities. Supports multiple models with automatic optimization and caching.
OpenRouter is a community-built MCP server published by stabgan that provides AI assistants with tools and capabilities via the Model Context Protocol. OpenRouter offers seamless access to diverse AI models for multimodal vision and language, with smart model selection an It is categorized under ai ml.
You can install OpenRouter in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
OpenRouter is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired OpenRouter into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
We evaluated OpenRouter against two servers with overlapping tools; this profile had the clearer scope statement.
OpenRouter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired OpenRouter into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
OpenRouter has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
OpenRouter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
According to our notes, OpenRouter benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
OpenRouter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
OpenRouter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated OpenRouter against two servers with overlapping tools; this profile had the clearer scope statement.
showing 1-10 of 71
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.
Text Chat:
Image Analysis:
Model Selection:
Performance Optimization:
Improved OS Compatibility:
MCP Configuration Support:
Robust Error Handling:
Image Processing Enhancements:
npm install -g @stabgan/openrouter-mcp-multimodal
docker run -i -e OPENROUTER_API_KEY=your-api-key-here stabgandocker/openrouter-mcp-multimodal:latest
Add one of the following configurations to your MCP settings file (e.g., cline_mcp_settings.json or claude_desktop_config.json):
{
"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"
}
}
}
}
{
"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"
}
}
}
}
{
"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"
]
}
}
}
{
"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"
}
}
}
}
For comprehensive examples of how to use this MCP server, check out the examples directory. We provide:
Each example comes with clear documentation and step-by-step instructions.
This project uses the following key dependencies:
@modelcontextprotocol/sdk: ^1.8.0 - Latest MCP SDK for tool implementationopenai: ^4.89.1 - OpenAI-compatible API client for OpenRoutersharp: ^0.33.5 - Fast image processing libraryaxios: ^1.8.4 - HTTP client for API requestsnode-fetch: ^3.3.2 - Modern fetch implementationNode.js 18 or later is required. All dependencies are regularly updated to ensure compatibility and security.
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"
}
}
]
}
]
}
});
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.