openrouterai
The OpenRouter MCP Server is an MCP server that provides seamless integration with OpenRouter.ai, allowing access to various AI models through a unified, type-safe interface. It incorporates caching, rate limiting, and error handling, enabling developers to efficiently utilize AI models. Notably, it features automatic model validation and performance optimization, along with structured error messages for clear error identification.
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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.
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
: Required. Your OpenRouter API key.OPENROUTER_DEFAULT_MODEL
: Optional. The default model to use if not specified in the request (e.g.,openrouter/auto
).OPENROUTER_MAX_TOKENS
: Optional. Default maximum number of tokens to generate ifmax_tokens
is not provided in the request.OPENROUTER_PROVIDER_QUANTIZATIONS
: Optional. Comma-separated list of default quantization levels to filter by (e.g.,fp16,int8
) ifprovider.quantizations
is not provided in the request. (Phase 1)OPENROUTER_PROVIDER_IGNORE
: Optional. Comma-separated list of default provider names to ignore (e.g.,mistralai,openai
) ifprovider.ignore
is not provided in the request. (Phase 1)OPENROUTER_PROVIDER_SORT
: Optional. Default sort order for providers ("price", "throughput", or "latency"). Overridden byprovider.sort
argument. (Phase 2)OPENROUTER_PROVIDER_ORDER
: Optional. Default prioritized list of provider IDs (JSON array string, e.g.,'["openai/gpt-4o", "anthropic/claude-3-opus"]'
). Overridden byprovider.order
argument. (Phase 2)OPENROUTER_PROVIDER_REQUIRE_PARAMETERS
: Optional. Default boolean (true
orfalse
) to only use providers supporting all specified request parameters. Overridden byprovider.require_parameters
argument. (Phase 2)OPENROUTER_PROVIDER_DATA_COLLECTION
: Optional. Default data collection policy ("allow" or "deny"). Overridden byprovider.data_collection
argument. (Phase 2)OPENROUTER_PROVIDER_ALLOW_FALLBACKS
: Optional. Default boolean (true
orfalse
) to control fallback behavior if preferred providers fail. Overridden byprovider.allow_fallbacks
argument. (Phase 2)
# Example .env file content
OPENROUTER_API_KEY=your-api-key-here
OPENROUTER_DEFAULT_MODEL=openrouter/auto
OPENROUTER_MAX_TOKENS=1024
OPENROUTER_PROVIDER_QUANTIZATIONS=fp16,int8
OPENROUTER_PROVIDER_IGNORE=openai,anthropic
OPENROUTER_PROVIDER_SORT=price
OPENROUTER_PROVIDER_ORDER='["openai/gpt-4o", "anthropic/claude-3-opus"]'
OPENROUTER_PROVIDER_REQUIRE_PARAMETERS=true
OPENROUTER_PROVIDER_DATA_COLLECTION=deny
OPENROUTER_PROVIDER_ALLOW_FALLBACKS=false
OPENROUTER_PROVIDER_QUANTIZATIONS=fp16,int8 OPENROUTER_PROVIDER_IGNORE=openai,anthropic
### Setup
Add to your MCP settings configuration file (`cline_mcp_settings.json` or `claude_desktop_config.json`):
```json
{
"mcpServers": {
"openrouterai": {
"command": "npx",
"args": ["@mcpservers/openrouterai"],
"env": {
"OPENROUTER_API_KEY": "your-api-key-here",
"OPENROUTER_DEFAULT_MODEL": "optional-default-model",
"OPENROUTER_MAX_TOKENS": "1024",
"OPENROUTER_PROVIDER_QUANTIZATIONS": "fp16,int8",
"OPENROUTER_PROVIDER_IGNORE": "openai,anthropic"
}
}
}
}
## Response Format
All tools return responses in a standardized structure:
```typescript
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
Sends a request to the OpenRouter Chat Completions API.
Input Schema:
model
(string, optional): The model to use (e.g.,openai/gpt-4o
,google/gemini-pro
). OverridesOPENROUTER_DEFAULT_MODEL
. Defaults toopenrouter/auto
if neither is set.- Model Suffixes: You can append
:nitro
to a model ID (e.g.,openai/gpt-4o:nitro
) to potentially route to faster, experimental versions if available. Append:floor
(e.g.,mistralai/mistral-7b-instruct:floor
) to use the cheapest available variant of a model, often useful for testing or low-cost tasks. Note: Availability of:nitro
and:floor
variants depends on OpenRouter.
- Model Suffixes: You can append
messages
(array, required): An array of message objects conforming to the OpenAI chat completion format.temperature
(number, optional): Sampling temperature. Defaults to 1.max_tokens
(number, optional): Maximum number of tokens to generate in the completion. OverridesOPENROUTER_MAX_TOKENS
.provider
(object, optional): Provider routing configuration. Overrides correspondingOPENROUTER_PROVIDER_*
environment variables.quantizations
(array of strings, optional): List of quantization levels to filter by (e.g.,["fp16", "int8"]
). Only models matching one of these levels will be considered. OverridesOPENROUTER_PROVIDER_QUANTIZATIONS
. (Phase 1)ignore
(array of strings, optional): List of provider names to exclude (e.g.,["openai", "anthropic"]
). Models from these providers will not be used. OverridesOPENROUTER_PROVIDER_IGNORE
. (Phase 1)sort
("price" | "throughput" | "latency", optional): Sort providers by the specified criteria. OverridesOPENROUTER_PROVIDER_SORT
. (Phase 2)order
(array of strings, optional): A prioritized list of provider IDs (e.g.,["openai/gpt-4o", "anthropic/claude-3-opus"]
). OverridesOPENROUTER_PROVIDER_ORDER
. (Phase 2)require_parameters
(boolean, optional): If true, only use providers that support all specified request parameters (like tools, functions, temperature). OverridesOPENROUTER_PROVIDER_REQUIRE_PARAMETERS
. (Phase 2)data_collection
("allow" | "deny", optional): Specify whether providers are allowed to collect data from the request. OverridesOPENROUTER_PROVIDER_DATA_COLLECTION
. (Phase 2)allow_fallbacks
(boolean, optional): If true (default), allows falling back to other providers if the preferred ones fail or are unavailable. If false, fails the request if preferred providers cannot be used. OverridesOPENROUTER_PROVIDER_ALLOW_FALLBACKS
. (Phase 2)
Example Usage:
{
"tool": "chat_completion",
"arguments": {
"model": "anthropic/claude-3-haiku",
"messages": [
{ "role": "user", "content": "Explain the concept of quantization in AI models." }
],
"max_tokens": 500,
"provider": {
"quantizations": ["fp16"],
"ignore": ["openai"],
"sort": "price",
"order": ["anthropic/claude-3-haiku", "google/gemini-pro"],
"require_parameters": true,
"allow_fallbacks": false
}
}
}
This example requests a completion from anthropic/claude-3-haiku
, limits the response to 500 tokens. It specifies provider routing options: prefer fp16
quantized models, ignore openai
providers, sort remaining providers by price
, prioritize anthropic/claude-3-haiku
then google/gemini-pro
, require the chosen provider to support all request parameters (like max_tokens
), and disable fallbacks (fail if the prioritized providers cannot fulfill the request).
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.
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