
OpenRouter MCP Server Featured
Provides integration with OpenRouter.ai, allowing access to various AI models through a unified interface.
heltonteixeira
Tools
chat_completion
Send a message to OpenRouter.ai and get a response
search_models
Search and filter OpenRouter.ai models based on various criteria
get_model_info
Get detailed information about a specific model
validate_model
Check if a model ID is valid
README
OpenRouter MCP Server
A Model Context Protocol (MCP) server providing seamless integration with OpenRouter.ai's diverse model ecosystem. Access various AI models through a unified, type-safe interface with built-in caching, rate limiting, and error handling.
<a href="https://glama.ai/mcp/servers/xdnmf8yei0"><img width="380" height="200" src="https://glama.ai/mcp/servers/xdnmf8yei0/badge" alt="OpenRouter Server MCP server" /></a>
Features
-
Model Access
- Direct access to all OpenRouter.ai models
- Automatic model validation and capability checking
- Default model configuration support
-
Performance Optimization
- Smart model information caching (1-hour expiry)
- Automatic rate limit management
- Exponential backoff for failed requests
-
Unified Response Format
- Consistent
ToolResult
structure for all responses - Clear error identification with
isError
flag - Structured error messages with context
- Consistent
Installation
pnpm install @mcpservers/openrouterai
Configuration
Prerequisites
- Get your OpenRouter API key from OpenRouter Keys
- Choose a default model (optional)
Environment Variables
OPENROUTER_API_KEY=your-api-key-here
OPENROUTER_DEFAULT_MODEL=optional-default-model
Setup
Add to your MCP settings configuration file (cline_mcp_settings.json
or claude_desktop_config.json
):
{
"mcpServers": {
"openrouterai": {
"command": "npx",
"args": ["@mcpservers/openrouterai"],
"env": {
"OPENROUTER_API_KEY": "your-api-key-here",
"OPENROUTER_DEFAULT_MODEL": "optional-default-model"
}
}
}
}
Response Format
All tools return responses in a standardized structure:
interface ToolResult {
isError: boolean;
content: Array<{
type: "text";
text: string; // JSON string or error message
}>;
}
Success Example:
{
"isError": false,
"content": [{
"type": "text",
"text": "{\"id\": \"gen-123\", ...}"
}]
}
Error Example:
{
"isError": true,
"content": [{
"type": "text",
"text": "Error: Model validation failed - 'invalid-model' not found"
}]
}
Available Tools
chat_completion
Send messages to OpenRouter.ai models:
interface ChatCompletionRequest {
model?: string;
messages: Array<{role: "user"|"system"|"assistant", content: string}>;
temperature?: number; // 0-2
}
// Response: ToolResult with chat completion data or error
search_models
Search and filter available models:
interface ModelSearchRequest {
query?: string;
provider?: string;
minContextLength?: number;
capabilities?: {
functions?: boolean;
vision?: boolean;
};
}
// Response: ToolResult with model list or error
get_model_info
Get detailed information about a specific model:
{
model: string; // Model identifier
}
validate_model
Check if a model ID is valid:
interface ModelValidationRequest {
model: string;
}
// Response:
// Success: { isError: false, valid: true }
// Error: { isError: true, error: "Model not found" }
Error Handling
The server provides structured errors with contextual information:
// Error response structure
{
isError: true,
content: [{
type: "text",
text: "Error: [Category] - Detailed message"
}]
}
Common Error Categories:
Validation Error
: Invalid input parametersAPI Error
: OpenRouter API communication issuesRate Limit
: Request throttling detectionInternal Error
: Server-side processing failures
Handling Responses:
async function handleResponse(result: ToolResult) {
if (result.isError) {
const errorMessage = result.content[0].text;
if (errorMessage.startsWith('Error: Rate Limit')) {
// Handle rate limiting
}
// Other error handling
} else {
const data = JSON.parse(result.content[0].text);
// Process successful response
}
}
Development
See CONTRIBUTING.md for detailed information about:
- Development setup
- Project structure
- Feature implementation
- Error handling guidelines
- Tool usage examples
# Install dependencies
pnpm install
# Build project
pnpm run build
# Run tests
pnpm test
Changelog
See CHANGELOG.md for recent updates including:
- Unified response format implementation
- Enhanced error handling system
- Type-safe interface improvements
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
AIO-MCP Server
🚀 All-in-one MCP server with AI search, RAG, and multi-service integrations (GitLab/Jira/Confluence/YouTube) for AI-enhanced development workflows. Folk from
Persistent Knowledge Graph
An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.
Hyperbrowser MCP Server
Welcome to Hyperbrowser, the Internet for AI. Hyperbrowser is the next-generation platform empowering AI agents and enabling effortless, scalable browser automation. Built specifically for AI developers, it eliminates the headaches of local infrastructure and performance bottlenecks, allowing you to
React MCP
react-mcp integrates with Claude Desktop, enabling the creation and modification of React apps based on user prompts