OpenRouter▌
by stabgan
OpenRouter offers seamless access to diverse AI models for multimodal vision and language, with smart model selection an
Provides seamless access to OpenRouter's diverse AI models, enabling multimodal interactions across vision and language models with intelligent model selection, caching, and robust error handling.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Developers building multimodal AI applications
- / Teams needing access to diverse AI models
- / Image analysis and visual content processing
- / Cross-platform AI integration projects
capabilities
- / Chat with various OpenRouter AI models
- / Analyze single or multiple images with custom questions
- / Search and filter available AI models
- / Process images from local files, URLs, or data URLs
- / Configure model parameters like temperature
- / Handle automatic image resizing and optimization
what it does
Connects to OpenRouter.ai's AI model ecosystem for text chat and image analysis capabilities. Supports multiple models with automatic optimization and caching.
about
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.
how to install
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.
license
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.
readme
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
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
- 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",
"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 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 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"
}
}
]
}
]
}
});
FAQ
- What is the OpenRouter MCP server?
- OpenRouter is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for OpenRouter?
- This profile displays 71 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.8★★★★★71 reviews- ★★★★★Amelia Lopez· Dec 20, 2024
We wired OpenRouter into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Olivia Gill· Dec 12, 2024
We evaluated OpenRouter against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Arjun Khan· Dec 12, 2024
OpenRouter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Dhruvi Jain· Dec 8, 2024
We wired OpenRouter into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Zaid Bansal· Dec 8, 2024
OpenRouter has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Oshnikdeep· Nov 27, 2024
OpenRouter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Isabella Li· Nov 27, 2024
According to our notes, OpenRouter benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Amelia Yang· Nov 11, 2024
OpenRouter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Olivia Bansal· Nov 3, 2024
OpenRouter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Anika Zhang· Nov 3, 2024
We evaluated OpenRouter against two servers with overlapping tools; this profile had the clearer scope statement.
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