Initial commit: Setting up OpenRouter MCP server for multimodal capabilities
This commit is contained in:
stabgan
84
.github/workflows/docker-publish.yml
vendored
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84
.github/workflows/docker-publish.yml
vendored
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@@ -0,0 +1,84 @@
|
||||
name: Docker Image and NPM Package CI/CD
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ main ]
|
||||
tags: [ 'v*' ]
|
||||
pull_request:
|
||||
branches: [ main ]
|
||||
|
||||
jobs:
|
||||
build-and-push-docker:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
packages: write
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v2
|
||||
|
||||
- name: Log in to GitHub Container Registry
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
registry: ghcr.io
|
||||
username: ${{ github.actor }}
|
||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Log in to Docker Hub
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
|
||||
- name: Extract metadata for Docker
|
||||
id: meta
|
||||
uses: docker/metadata-action@v4
|
||||
with:
|
||||
images: |
|
||||
ghcr.io/stabgan/openrouter-mcp-multimodal
|
||||
stabgan/openrouter-mcp-multimodal
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||||
tags: |
|
||||
type=semver,pattern={{version}}
|
||||
type=ref,event=branch
|
||||
type=sha,format=short
|
||||
type=raw,value=latest,enable={{is_default_branch}}
|
||||
|
||||
- name: Build and push Docker image
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: ${{ steps.meta.outputs.tags }}
|
||||
labels: ${{ steps.meta.outputs.labels }}
|
||||
cache-from: type=gha
|
||||
cache-to: type=gha,mode=max
|
||||
|
||||
publish-npm:
|
||||
runs-on: ubuntu-latest
|
||||
# Only run on tagged versions (v*)
|
||||
if: startsWith(github.ref, 'refs/tags/v')
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Setup Node.js
|
||||
uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: '18.x'
|
||||
registry-url: 'https://registry.npmjs.org/'
|
||||
|
||||
- name: Install dependencies
|
||||
run: npm ci
|
||||
|
||||
- name: Build package
|
||||
run: npm run build
|
||||
|
||||
- name: Publish to NPM
|
||||
run: npm publish --access=public
|
||||
env:
|
||||
NODE_AUTH_TOKEN: ${{ secrets.NPMJS_TOKEN }}
|
||||
50
.gitignore
vendored
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50
.gitignore
vendored
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@@ -0,0 +1,50 @@
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||||
# Logs
|
||||
logs
|
||||
*.log
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||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
|
||||
# Dependency directories
|
||||
node_modules/
|
||||
|
||||
# TypeScript cache
|
||||
*.tsbuildinfo
|
||||
|
||||
# Optional npm cache directory
|
||||
.npm
|
||||
|
||||
# Optional eslint cache
|
||||
.eslintcache
|
||||
|
||||
# Build outputs
|
||||
dist
|
||||
build
|
||||
|
||||
# dotenv environment variables file
|
||||
.env
|
||||
.env.test
|
||||
.env.local
|
||||
|
||||
# IDE files
|
||||
.idea/
|
||||
.vscode/
|
||||
*.iml
|
||||
*.iws
|
||||
*.ipr
|
||||
*.swp
|
||||
*.swo
|
||||
|
||||
# macOS
|
||||
.DS_Store
|
||||
|
||||
# Windows
|
||||
Thumbs.db
|
||||
ehthumbs.db
|
||||
ehthumbs_vista.db
|
||||
*.stackdump
|
||||
[Dd]esktop.ini
|
||||
|
||||
# Testing
|
||||
coverage/
|
||||
.nyc_output/
|
||||
29
Dockerfile
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29
Dockerfile
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|
||||
FROM node:18-alpine
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# Install dependencies for sharp
|
||||
RUN apk add --no-cache \
|
||||
g++ \
|
||||
make \
|
||||
python3
|
||||
|
||||
# Copy package files and install dependencies
|
||||
COPY package*.json ./
|
||||
RUN npm install
|
||||
|
||||
# Copy source code
|
||||
COPY . .
|
||||
|
||||
# Build TypeScript code
|
||||
RUN npm run build
|
||||
|
||||
# Default environment variables
|
||||
ENV NODE_ENV=production
|
||||
|
||||
# The API key should be passed at runtime
|
||||
# ENV OPENROUTER_API_KEY=your-api-key-here
|
||||
# ENV OPENROUTER_DEFAULT_MODEL=your-default-model
|
||||
|
||||
# Run the server
|
||||
CMD ["node", "dist/index.js"]
|
||||
313
README.md
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313
README.md
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@@ -0,0 +1,313 @@
|
||||
# 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
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||||
- 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
|
||||
|
||||
## Quick Start Configuration
|
||||
|
||||
### Prerequisites
|
||||
|
||||
1. Get your OpenRouter API key from [OpenRouter Keys](https://openrouter.ai/keys)
|
||||
2. 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)
|
||||
|
||||
```json
|
||||
{
|
||||
"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)
|
||||
|
||||
```json
|
||||
{
|
||||
"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
|
||||
|
||||
```json
|
||||
{
|
||||
"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",
|
||||
"stabgan/openrouter-mcp-multimodal:latest"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Option 4: Using Smithery (recommended)
|
||||
|
||||
```json
|
||||
{
|
||||
"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:
|
||||
|
||||
```javascript
|
||||
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:
|
||||
|
||||
```javascript
|
||||
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"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
});
|
||||
```
|
||||
|
||||
### analyze_image
|
||||
|
||||
Analyze a single image with an optional question:
|
||||
|
||||
```javascript
|
||||
use_mcp_tool({
|
||||
server_name: "openrouter",
|
||||
tool_name: "analyze_image",
|
||||
arguments: {
|
||||
image_path: "/absolute/path/to/image.jpg",
|
||||
question: "What objects are in this image?", // Optional
|
||||
model: "anthropic/claude-3.5-sonnet" // Optional if default is set
|
||||
}
|
||||
});
|
||||
```
|
||||
|
||||
### multi_image_analysis
|
||||
|
||||
Analyze multiple images with a single prompt:
|
||||
|
||||
```javascript
|
||||
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:
|
||||
|
||||
```javascript
|
||||
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:
|
||||
|
||||
```javascript
|
||||
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:
|
||||
|
||||
```javascript
|
||||
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:
|
||||
|
||||
```json
|
||||
{
|
||||
"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:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/stabgan/openrouter-mcp-multimodal.git
|
||||
cd openrouter-mcp-multimodal
|
||||
npm install
|
||||
npm run build
|
||||
```
|
||||
|
||||
## License
|
||||
|
||||
MIT License
|
||||
60
package.json
Normal file
60
package.json
Normal file
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"name": "@stabgan/openrouter-mcp-multimodal",
|
||||
"version": "1.0.0",
|
||||
"description": "MCP server for OpenRouter providing text chat and image analysis tools",
|
||||
"type": "module",
|
||||
"main": "dist/index.js",
|
||||
"bin": {
|
||||
"openrouter-multimodal": "dist/index.js"
|
||||
},
|
||||
"files": [
|
||||
"dist",
|
||||
"README.md",
|
||||
"LICENSE"
|
||||
],
|
||||
"scripts": {
|
||||
"build": "tsc && shx chmod +x dist/*.js",
|
||||
"prepare": "npm run build",
|
||||
"start": "node dist/index.js",
|
||||
"watch": "tsc --watch"
|
||||
},
|
||||
"keywords": [
|
||||
"mcp",
|
||||
"openrouter",
|
||||
"ai",
|
||||
"llm",
|
||||
"vision",
|
||||
"image-analysis",
|
||||
"modelcontextprotocol"
|
||||
],
|
||||
"author": "stabgan",
|
||||
"license": "MIT",
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "git+https://github.com/stabgan/openrouter-mcp-multimodal.git"
|
||||
},
|
||||
"bugs": {
|
||||
"url": "https://github.com/stabgan/openrouter-mcp-multimodal/issues"
|
||||
},
|
||||
"homepage": "https://github.com/stabgan/openrouter-mcp-multimodal#readme",
|
||||
"engines": {
|
||||
"node": ">=18.0.0"
|
||||
},
|
||||
"dependencies": {
|
||||
"@modelcontextprotocol/sdk": "^1.4.1",
|
||||
"axios": "^1.7.9",
|
||||
"node-fetch": "^3.3.2",
|
||||
"openai": "^4.83.0",
|
||||
"sharp": "^0.33.3"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/node": "^22.13.1",
|
||||
"@types/sharp": "^0.32.0",
|
||||
"shx": "^0.3.4",
|
||||
"typescript": "^5.7.3"
|
||||
},
|
||||
"overrides": {
|
||||
"uri-js": "npm:uri-js-replace",
|
||||
"whatwg-url": "^14.1.0"
|
||||
}
|
||||
}
|
||||
43
smithery.yaml
Normal file
43
smithery.yaml
Normal file
@@ -0,0 +1,43 @@
|
||||
name: openrouter-mcp-multimodal
|
||||
version: 1.0.0
|
||||
description: MCP server for OpenRouter providing text chat and image analysis tools
|
||||
|
||||
image:
|
||||
name: ghcr.io/stabgan/openrouter-mcp-multimodal
|
||||
tag: latest
|
||||
|
||||
entrypoint: ["node", "dist/index.js"]
|
||||
|
||||
build:
|
||||
dockerfile: Dockerfile
|
||||
|
||||
publish:
|
||||
smithery: true
|
||||
dockerhub:
|
||||
enabled: true
|
||||
username: stabgan
|
||||
repository: openrouter-mcp-multimodal
|
||||
|
||||
config:
|
||||
env:
|
||||
- name: OPENROUTER_API_KEY
|
||||
description: OpenRouter API key for authentication
|
||||
required: true
|
||||
- name: OPENROUTER_DEFAULT_MODEL
|
||||
description: Default model to use if none specified in requests
|
||||
required: false
|
||||
defaultValue: "anthropic/claude-3.5-sonnet"
|
||||
|
||||
documentation:
|
||||
description: |
|
||||
An MCP server that provides chat and image analysis capabilities through OpenRouter.ai's diverse model ecosystem.
|
||||
Supports text chat, single and multi-image analysis, and model search/validation.
|
||||
usage: |
|
||||
This MCP server provides the following tools:
|
||||
- chat_completion: Send text or multimodal messages to OpenRouter
|
||||
- analyze_image: Analyze a single image with an optional question
|
||||
- multi_image_analysis: Analyze multiple images with a single prompt
|
||||
- search_models: Search and filter available models
|
||||
- get_model_info: Get detailed information about a specific model
|
||||
- validate_model: Check if a model ID is valid
|
||||
link: https://github.com/stabgan/openrouter-mcp-multimodal
|
||||
67
src/index.ts
Normal file
67
src/index.ts
Normal file
@@ -0,0 +1,67 @@
|
||||
#!/usr/bin/env node
|
||||
// OpenRouter Multimodal MCP Server
|
||||
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
|
||||
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
|
||||
|
||||
import { ToolHandlers } from './tool-handlers.js';
|
||||
|
||||
class OpenRouterMultimodalServer {
|
||||
private server: Server;
|
||||
private toolHandlers!: ToolHandlers; // Using definite assignment assertion
|
||||
|
||||
constructor() {
|
||||
// Get API key and default model from environment variables
|
||||
const apiKey = process.env.OPENROUTER_API_KEY;
|
||||
const defaultModel = process.env.OPENROUTER_DEFAULT_MODEL;
|
||||
|
||||
// Check if API key is provided
|
||||
if (!apiKey) {
|
||||
throw new Error('OPENROUTER_API_KEY environment variable is required');
|
||||
}
|
||||
|
||||
// Initialize the server
|
||||
this.server = new Server(
|
||||
{
|
||||
name: 'openrouter-multimodal-server',
|
||||
version: '1.0.0',
|
||||
},
|
||||
{
|
||||
capabilities: {
|
||||
tools: {},
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
// Set up error handling
|
||||
this.server.onerror = (error) => console.error('[MCP Error]', error);
|
||||
|
||||
// Initialize tool handlers
|
||||
this.toolHandlers = new ToolHandlers(
|
||||
this.server,
|
||||
apiKey,
|
||||
defaultModel
|
||||
);
|
||||
|
||||
process.on('SIGINT', async () => {
|
||||
await this.server.close();
|
||||
process.exit(0);
|
||||
});
|
||||
}
|
||||
|
||||
async run() {
|
||||
const transport = new StdioServerTransport();
|
||||
await this.server.connect(transport);
|
||||
console.error('OpenRouter Multimodal MCP server running on stdio');
|
||||
console.error('Using API key from environment variable');
|
||||
console.error('Note: To use OpenRouter Multimodal, add the API key to your environment variables:');
|
||||
console.error(' OPENROUTER_API_KEY=your-api-key');
|
||||
if (process.env.OPENROUTER_DEFAULT_MODEL) {
|
||||
console.error(` Using default model: ${process.env.OPENROUTER_DEFAULT_MODEL}`);
|
||||
} else {
|
||||
console.error(' No default model set. You will need to specify a model in each request.');
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const server = new OpenRouterMultimodalServer();
|
||||
server.run().catch(console.error);
|
||||
167
src/model-cache.ts
Normal file
167
src/model-cache.ts
Normal file
@@ -0,0 +1,167 @@
|
||||
/**
|
||||
* ModelCache - Caches OpenRouter model data to reduce API calls
|
||||
*/
|
||||
export class ModelCache {
|
||||
private static instance: ModelCache;
|
||||
private models: Record<string, any>;
|
||||
private lastFetchTime: number;
|
||||
private cacheExpiryTime: number; // in milliseconds (1 hour = 3600000)
|
||||
|
||||
private constructor() {
|
||||
this.models = {};
|
||||
this.lastFetchTime = 0;
|
||||
this.cacheExpiryTime = 3600000; // 1 hour
|
||||
}
|
||||
|
||||
/**
|
||||
* Get singleton instance
|
||||
*/
|
||||
public static getInstance(): ModelCache {
|
||||
if (!ModelCache.instance) {
|
||||
ModelCache.instance = new ModelCache();
|
||||
}
|
||||
return ModelCache.instance;
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if the cache is valid
|
||||
*/
|
||||
public isCacheValid(): boolean {
|
||||
return (
|
||||
Object.keys(this.models).length > 0 &&
|
||||
Date.now() - this.lastFetchTime < this.cacheExpiryTime
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Store all models
|
||||
*/
|
||||
public setModels(models: any[]): void {
|
||||
this.models = {};
|
||||
for (const model of models) {
|
||||
this.models[model.id] = model;
|
||||
}
|
||||
this.lastFetchTime = Date.now();
|
||||
}
|
||||
|
||||
/**
|
||||
* Get all cached models
|
||||
*/
|
||||
public getAllModels(): any[] {
|
||||
return Object.values(this.models);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get a specific model by ID
|
||||
*/
|
||||
public getModel(modelId: string): any | null {
|
||||
return this.models[modelId] || null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if a model exists
|
||||
*/
|
||||
public hasModel(modelId: string): boolean {
|
||||
return !!this.models[modelId];
|
||||
}
|
||||
|
||||
/**
|
||||
* Search models based on criteria
|
||||
*/
|
||||
public searchModels(params: {
|
||||
query?: string;
|
||||
provider?: string;
|
||||
minContextLength?: number;
|
||||
maxContextLength?: number;
|
||||
maxPromptPrice?: number;
|
||||
maxCompletionPrice?: number;
|
||||
capabilities?: {
|
||||
functions?: boolean;
|
||||
tools?: boolean;
|
||||
vision?: boolean;
|
||||
json_mode?: boolean;
|
||||
};
|
||||
limit?: number;
|
||||
}): any[] {
|
||||
let results = this.getAllModels();
|
||||
|
||||
// Apply text search
|
||||
if (params.query) {
|
||||
const query = params.query.toLowerCase();
|
||||
results = results.filter((model) =>
|
||||
model.id.toLowerCase().includes(query) ||
|
||||
(model.description && model.description.toLowerCase().includes(query)) ||
|
||||
(model.provider && model.provider.toLowerCase().includes(query))
|
||||
);
|
||||
}
|
||||
|
||||
// Filter by provider
|
||||
if (params.provider) {
|
||||
results = results.filter((model) =>
|
||||
model.provider && model.provider.toLowerCase() === params.provider!.toLowerCase()
|
||||
);
|
||||
}
|
||||
|
||||
// Filter by context length
|
||||
if (params.minContextLength) {
|
||||
results = results.filter(
|
||||
(model) => model.context_length >= params.minContextLength
|
||||
);
|
||||
}
|
||||
|
||||
if (params.maxContextLength) {
|
||||
results = results.filter(
|
||||
(model) => model.context_length <= params.maxContextLength
|
||||
);
|
||||
}
|
||||
|
||||
// Filter by price
|
||||
if (params.maxPromptPrice) {
|
||||
results = results.filter(
|
||||
(model) =>
|
||||
!model.pricing?.prompt || model.pricing.prompt <= params.maxPromptPrice
|
||||
);
|
||||
}
|
||||
|
||||
if (params.maxCompletionPrice) {
|
||||
results = results.filter(
|
||||
(model) =>
|
||||
!model.pricing?.completion ||
|
||||
model.pricing.completion <= params.maxCompletionPrice
|
||||
);
|
||||
}
|
||||
|
||||
// Filter by capabilities
|
||||
if (params.capabilities) {
|
||||
if (params.capabilities.functions) {
|
||||
results = results.filter(
|
||||
(model) => model.capabilities?.function_calling
|
||||
);
|
||||
}
|
||||
if (params.capabilities.tools) {
|
||||
results = results.filter((model) => model.capabilities?.tools);
|
||||
}
|
||||
if (params.capabilities.vision) {
|
||||
results = results.filter((model) => model.capabilities?.vision);
|
||||
}
|
||||
if (params.capabilities.json_mode) {
|
||||
results = results.filter((model) => model.capabilities?.json_mode);
|
||||
}
|
||||
}
|
||||
|
||||
// Apply limit
|
||||
if (params.limit && params.limit > 0) {
|
||||
results = results.slice(0, params.limit);
|
||||
}
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
/**
|
||||
* Reset the cache
|
||||
*/
|
||||
public resetCache(): void {
|
||||
this.models = {};
|
||||
this.lastFetchTime = 0;
|
||||
}
|
||||
}
|
||||
130
src/openrouter-api.ts
Normal file
130
src/openrouter-api.ts
Normal file
@@ -0,0 +1,130 @@
|
||||
import axios, { AxiosError, AxiosInstance } from 'axios';
|
||||
import { McpError } from '@modelcontextprotocol/sdk/types.js';
|
||||
|
||||
/**
|
||||
* Client for interacting with the OpenRouter API
|
||||
*/
|
||||
export class OpenRouterAPIClient {
|
||||
private apiKey: string;
|
||||
private axiosInstance: AxiosInstance;
|
||||
private retryCount: number = 3;
|
||||
private retryDelay: number = 1000; // Initial delay in ms
|
||||
|
||||
constructor(apiKey: string) {
|
||||
this.apiKey = apiKey;
|
||||
this.axiosInstance = axios.create({
|
||||
baseURL: 'https://openrouter.ai/api/v1',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${this.apiKey}`,
|
||||
'Content-Type': 'application/json',
|
||||
'HTTP-Referer': 'https://github.com/stabgan/openrouter-mcp-multimodal',
|
||||
'X-Title': 'OpenRouter MCP Multimodal Server'
|
||||
},
|
||||
timeout: 60000 // 60 seconds timeout
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Get all available models from OpenRouter
|
||||
*/
|
||||
public async getModels(): Promise<any[]> {
|
||||
try {
|
||||
const response = await this.axiosInstance.get('/models');
|
||||
return response.data.data;
|
||||
} catch (error) {
|
||||
this.handleRequestError(error);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Send a request to the OpenRouter API with retry functionality
|
||||
*/
|
||||
public async request(endpoint: string, method: string, data?: any): Promise<any> {
|
||||
let lastError: Error | null = null;
|
||||
let retries = 0;
|
||||
|
||||
while (retries <= this.retryCount) {
|
||||
try {
|
||||
const response = await this.axiosInstance.request({
|
||||
url: endpoint,
|
||||
method,
|
||||
data
|
||||
});
|
||||
|
||||
return response.data;
|
||||
} catch (error) {
|
||||
lastError = this.handleRetryableError(error, retries);
|
||||
retries++;
|
||||
|
||||
if (retries <= this.retryCount) {
|
||||
// Exponential backoff with jitter
|
||||
const delay = this.retryDelay * Math.pow(2, retries - 1) * (0.5 + Math.random() * 0.5);
|
||||
console.error(`Retrying in ${Math.round(delay)}ms (${retries}/${this.retryCount})`);
|
||||
await new Promise(resolve => setTimeout(resolve, delay));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If we get here, all retries failed
|
||||
throw lastError || new Error('Request failed after multiple retries');
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle retryable errors
|
||||
*/
|
||||
private handleRetryableError(error: any, retryCount: number): Error {
|
||||
if (axios.isAxiosError(error)) {
|
||||
const axiosError = error as AxiosError;
|
||||
|
||||
// Rate limiting (429) or server errors (5xx)
|
||||
if (axiosError.response?.status === 429 || (axiosError.response?.status && axiosError.response.status >= 500)) {
|
||||
console.error(`Request error (retry ${retryCount}): ${axiosError.message}`);
|
||||
if (axiosError.response?.status === 429) {
|
||||
console.error('Rate limit exceeded. Retrying with backoff...');
|
||||
}
|
||||
return new Error(`OpenRouter API error: ${axiosError.response?.status} ${axiosError.message}`);
|
||||
}
|
||||
|
||||
// For other status codes, don't retry
|
||||
if (axiosError.response) {
|
||||
const responseData = axiosError.response.data as any;
|
||||
const message = responseData?.error?.message || axiosError.message;
|
||||
throw new McpError('RequestFailed', `OpenRouter API error: ${message}`);
|
||||
}
|
||||
}
|
||||
|
||||
// Network errors should be retried
|
||||
console.error(`Network error (retry ${retryCount}): ${error.message}`);
|
||||
return new Error(`Network error: ${error.message}`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle request errors
|
||||
*/
|
||||
private handleRequestError(error: any): never {
|
||||
console.error('Error in OpenRouter API request:', error);
|
||||
|
||||
if (axios.isAxiosError(error)) {
|
||||
const axiosError = error as AxiosError;
|
||||
|
||||
if (axiosError.response) {
|
||||
const status = axiosError.response.status;
|
||||
const responseData = axiosError.response.data as any;
|
||||
const message = responseData?.error?.message || axiosError.message;
|
||||
|
||||
if (status === 401 || status === 403) {
|
||||
throw new McpError('Unauthorized', `Authentication error: ${message}`);
|
||||
} else if (status === 429) {
|
||||
throw new McpError('RateLimitExceeded', `Rate limit exceeded: ${message}`);
|
||||
} else {
|
||||
throw new McpError('RequestFailed', `OpenRouter API error (${status}): ${message}`);
|
||||
}
|
||||
} else if (axiosError.request) {
|
||||
throw new McpError('NetworkError', `Network error: ${axiosError.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
throw new McpError('UnknownError', `Unknown error: ${error.message || 'No error message'}`);
|
||||
}
|
||||
}
|
||||
347
src/tool-handlers.ts
Normal file
347
src/tool-handlers.ts
Normal file
@@ -0,0 +1,347 @@
|
||||
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
|
||||
import {
|
||||
CallToolRequestSchema,
|
||||
ErrorCode,
|
||||
ListToolsRequestSchema,
|
||||
McpError,
|
||||
} from '@modelcontextprotocol/sdk/types.js';
|
||||
import OpenAI from 'openai';
|
||||
|
||||
import { ModelCache } from './model-cache.js';
|
||||
import { OpenRouterAPIClient } from './openrouter-api.js';
|
||||
|
||||
// Import tool handlers
|
||||
import { handleChatCompletion, ChatCompletionToolRequest } from './tool-handlers/chat-completion.js';
|
||||
import { handleSearchModels, SearchModelsToolRequest } from './tool-handlers/search-models.js';
|
||||
import { handleGetModelInfo, GetModelInfoToolRequest } from './tool-handlers/get-model-info.js';
|
||||
import { handleValidateModel, ValidateModelToolRequest } from './tool-handlers/validate-model.js';
|
||||
import { handleAnalyzeImage, AnalyzeImageToolRequest } from './tool-handlers/analyze-image.js';
|
||||
import { handleMultiImageAnalysis, MultiImageAnalysisToolRequest } from './tool-handlers/multi-image-analysis.js';
|
||||
|
||||
export class ToolHandlers {
|
||||
private server: Server;
|
||||
private openai: OpenAI;
|
||||
private modelCache: ModelCache;
|
||||
private apiClient: OpenRouterAPIClient;
|
||||
private defaultModel?: string;
|
||||
|
||||
constructor(
|
||||
server: Server,
|
||||
apiKey: string,
|
||||
defaultModel?: string
|
||||
) {
|
||||
this.server = server;
|
||||
this.modelCache = ModelCache.getInstance();
|
||||
this.apiClient = new OpenRouterAPIClient(apiKey);
|
||||
this.defaultModel = defaultModel;
|
||||
|
||||
this.openai = new OpenAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: 'https://openrouter.ai/api/v1',
|
||||
defaultHeaders: {
|
||||
'HTTP-Referer': 'https://github.com/stabgan/openrouter-mcp-multimodal',
|
||||
'X-Title': 'OpenRouter MCP Multimodal Server',
|
||||
},
|
||||
});
|
||||
|
||||
this.setupToolHandlers();
|
||||
}
|
||||
|
||||
private setupToolHandlers() {
|
||||
this.server.setRequestHandler(ListToolsRequestSchema, async () => ({
|
||||
tools: [
|
||||
// Chat Completion Tool
|
||||
{
|
||||
name: 'chat_completion',
|
||||
description: 'Send a message to OpenRouter.ai and get a response',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
model: {
|
||||
type: 'string',
|
||||
description: 'The model to use (e.g., "google/gemini-2.5-pro-exp-03-25:free", "undi95/toppy-m-7b:free"). If not provided, uses the default model if set.',
|
||||
},
|
||||
messages: {
|
||||
type: 'array',
|
||||
description: 'An array of conversation messages with roles and content',
|
||||
minItems: 1,
|
||||
maxItems: 100,
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
role: {
|
||||
type: 'string',
|
||||
enum: ['system', 'user', 'assistant'],
|
||||
description: 'The role of the message sender',
|
||||
},
|
||||
content: {
|
||||
oneOf: [
|
||||
{
|
||||
type: 'string',
|
||||
description: 'The text content of the message',
|
||||
},
|
||||
{
|
||||
type: 'array',
|
||||
description: 'Array of content parts for multimodal messages (text and images)',
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
type: {
|
||||
type: 'string',
|
||||
enum: ['text', 'image_url'],
|
||||
description: 'The type of content (text or image)',
|
||||
},
|
||||
text: {
|
||||
type: 'string',
|
||||
description: 'The text content (for text type)',
|
||||
},
|
||||
image_url: {
|
||||
type: 'object',
|
||||
description: 'The image URL object (for image_url type)',
|
||||
properties: {
|
||||
url: {
|
||||
type: 'string',
|
||||
description: 'URL of the image (can be a data URL with base64)',
|
||||
},
|
||||
},
|
||||
required: ['url'],
|
||||
},
|
||||
},
|
||||
required: ['type'],
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
required: ['role', 'content'],
|
||||
},
|
||||
},
|
||||
temperature: {
|
||||
type: 'number',
|
||||
description: 'Sampling temperature (0-2)',
|
||||
minimum: 0,
|
||||
maximum: 2,
|
||||
},
|
||||
},
|
||||
required: ['messages'],
|
||||
},
|
||||
maxContextTokens: 200000
|
||||
},
|
||||
|
||||
// Image Analysis Tool
|
||||
{
|
||||
name: 'analyze_image',
|
||||
description: 'Analyze an image using OpenRouter vision models',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
image_path: {
|
||||
type: 'string',
|
||||
description: 'Path to the image file to analyze (must be an absolute path)',
|
||||
},
|
||||
question: {
|
||||
type: 'string',
|
||||
description: 'Question to ask about the image',
|
||||
},
|
||||
model: {
|
||||
type: 'string',
|
||||
description: 'OpenRouter model to use (e.g., "anthropic/claude-3.5-sonnet")',
|
||||
},
|
||||
},
|
||||
required: ['image_path'],
|
||||
},
|
||||
},
|
||||
|
||||
// Multi-Image Analysis Tool
|
||||
{
|
||||
name: 'multi_image_analysis',
|
||||
description: 'Analyze multiple images at once with a single prompt and receive detailed responses',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
images: {
|
||||
type: 'array',
|
||||
description: 'Array of image objects to analyze',
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
url: {
|
||||
type: 'string',
|
||||
description: 'URL or data URL of the image (can be a file:// URL to read from local filesystem)',
|
||||
},
|
||||
alt: {
|
||||
type: 'string',
|
||||
description: 'Optional alt text or description of the image',
|
||||
},
|
||||
},
|
||||
required: ['url'],
|
||||
},
|
||||
},
|
||||
prompt: {
|
||||
type: 'string',
|
||||
description: 'Prompt for analyzing the images',
|
||||
},
|
||||
markdown_response: {
|
||||
type: 'boolean',
|
||||
description: 'Whether to format the response in Markdown (default: true)',
|
||||
default: true,
|
||||
},
|
||||
model: {
|
||||
type: 'string',
|
||||
description: 'OpenRouter model to use (defaults to claude-3.5-sonnet if not specified)',
|
||||
},
|
||||
},
|
||||
required: ['images', 'prompt'],
|
||||
},
|
||||
},
|
||||
|
||||
// Search Models Tool
|
||||
{
|
||||
name: 'search_models',
|
||||
description: 'Search and filter OpenRouter.ai models based on various criteria',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
query: {
|
||||
type: 'string',
|
||||
description: 'Optional search query to filter by name, description, or provider',
|
||||
},
|
||||
provider: {
|
||||
type: 'string',
|
||||
description: 'Filter by specific provider (e.g., "anthropic", "openai", "cohere")',
|
||||
},
|
||||
minContextLength: {
|
||||
type: 'number',
|
||||
description: 'Minimum context length in tokens',
|
||||
},
|
||||
maxContextLength: {
|
||||
type: 'number',
|
||||
description: 'Maximum context length in tokens',
|
||||
},
|
||||
maxPromptPrice: {
|
||||
type: 'number',
|
||||
description: 'Maximum price per 1K tokens for prompts',
|
||||
},
|
||||
maxCompletionPrice: {
|
||||
type: 'number',
|
||||
description: 'Maximum price per 1K tokens for completions',
|
||||
},
|
||||
capabilities: {
|
||||
type: 'object',
|
||||
description: 'Filter by model capabilities',
|
||||
properties: {
|
||||
functions: {
|
||||
type: 'boolean',
|
||||
description: 'Requires function calling capability',
|
||||
},
|
||||
tools: {
|
||||
type: 'boolean',
|
||||
description: 'Requires tools capability',
|
||||
},
|
||||
vision: {
|
||||
type: 'boolean',
|
||||
description: 'Requires vision capability',
|
||||
},
|
||||
json_mode: {
|
||||
type: 'boolean',
|
||||
description: 'Requires JSON mode capability',
|
||||
}
|
||||
}
|
||||
},
|
||||
limit: {
|
||||
type: 'number',
|
||||
description: 'Maximum number of results to return (default: 10)',
|
||||
minimum: 1,
|
||||
maximum: 50
|
||||
}
|
||||
}
|
||||
},
|
||||
},
|
||||
|
||||
// Get Model Info Tool
|
||||
{
|
||||
name: 'get_model_info',
|
||||
description: 'Get detailed information about a specific model',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
model: {
|
||||
type: 'string',
|
||||
description: 'The model ID to get information for',
|
||||
},
|
||||
},
|
||||
required: ['model'],
|
||||
},
|
||||
},
|
||||
|
||||
// Validate Model Tool
|
||||
{
|
||||
name: 'validate_model',
|
||||
description: 'Check if a model ID is valid',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
model: {
|
||||
type: 'string',
|
||||
description: 'The model ID to validate',
|
||||
},
|
||||
},
|
||||
required: ['model'],
|
||||
},
|
||||
},
|
||||
],
|
||||
}));
|
||||
|
||||
this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
|
||||
switch (request.params.name) {
|
||||
case 'chat_completion':
|
||||
return handleChatCompletion({
|
||||
params: {
|
||||
arguments: request.params.arguments as unknown as ChatCompletionToolRequest
|
||||
}
|
||||
}, this.openai, this.defaultModel);
|
||||
|
||||
case 'analyze_image':
|
||||
return handleAnalyzeImage({
|
||||
params: {
|
||||
arguments: request.params.arguments as unknown as AnalyzeImageToolRequest
|
||||
}
|
||||
}, this.openai, this.defaultModel);
|
||||
|
||||
case 'multi_image_analysis':
|
||||
return handleMultiImageAnalysis({
|
||||
params: {
|
||||
arguments: request.params.arguments as unknown as MultiImageAnalysisToolRequest
|
||||
}
|
||||
}, this.openai, this.defaultModel);
|
||||
|
||||
case 'search_models':
|
||||
return handleSearchModels({
|
||||
params: {
|
||||
arguments: request.params.arguments as SearchModelsToolRequest
|
||||
}
|
||||
}, this.apiClient, this.modelCache);
|
||||
|
||||
case 'get_model_info':
|
||||
return handleGetModelInfo({
|
||||
params: {
|
||||
arguments: request.params.arguments as unknown as GetModelInfoToolRequest
|
||||
}
|
||||
}, this.modelCache);
|
||||
|
||||
case 'validate_model':
|
||||
return handleValidateModel({
|
||||
params: {
|
||||
arguments: request.params.arguments as unknown as ValidateModelToolRequest
|
||||
}
|
||||
}, this.modelCache);
|
||||
|
||||
default:
|
||||
throw new McpError(
|
||||
ErrorCode.MethodNotFound,
|
||||
`Unknown tool: ${request.params.name}`
|
||||
);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
116
src/tool-handlers/analyze-image.ts
Normal file
116
src/tool-handlers/analyze-image.ts
Normal file
@@ -0,0 +1,116 @@
|
||||
import path from 'path';
|
||||
import { promises as fs } from 'fs';
|
||||
import sharp from 'sharp';
|
||||
import { McpError } from '@modelcontextprotocol/sdk/types.js';
|
||||
import OpenAI from 'openai';
|
||||
|
||||
export interface AnalyzeImageToolRequest {
|
||||
image_path: string;
|
||||
question?: string;
|
||||
model?: string;
|
||||
}
|
||||
|
||||
export async function handleAnalyzeImage(
|
||||
request: { params: { arguments: AnalyzeImageToolRequest } },
|
||||
openai: OpenAI,
|
||||
defaultModel?: string
|
||||
) {
|
||||
const args = request.params.arguments;
|
||||
|
||||
try {
|
||||
// Validate image path
|
||||
const imagePath = args.image_path;
|
||||
if (!path.isAbsolute(imagePath)) {
|
||||
throw new McpError('InvalidParams', 'Image path must be absolute');
|
||||
}
|
||||
|
||||
// Read image file
|
||||
const imageBuffer = await fs.readFile(imagePath);
|
||||
console.error(`Successfully read image buffer of size: ${imageBuffer.length}`);
|
||||
|
||||
// Get image metadata
|
||||
const metadata = await sharp(imageBuffer).metadata();
|
||||
console.error('Image metadata:', metadata);
|
||||
|
||||
// Calculate dimensions to keep base64 size reasonable
|
||||
const MAX_DIMENSION = 800; // Larger than original example for better quality
|
||||
const JPEG_QUALITY = 80; // Higher quality
|
||||
let resizedBuffer = imageBuffer;
|
||||
|
||||
if (metadata.width && metadata.height) {
|
||||
const largerDimension = Math.max(metadata.width, metadata.height);
|
||||
if (largerDimension > MAX_DIMENSION) {
|
||||
const resizeOptions = metadata.width > metadata.height
|
||||
? { width: MAX_DIMENSION }
|
||||
: { height: MAX_DIMENSION };
|
||||
|
||||
resizedBuffer = await sharp(imageBuffer)
|
||||
.resize(resizeOptions)
|
||||
.jpeg({ quality: JPEG_QUALITY })
|
||||
.toBuffer();
|
||||
} else {
|
||||
resizedBuffer = await sharp(imageBuffer)
|
||||
.jpeg({ quality: JPEG_QUALITY })
|
||||
.toBuffer();
|
||||
}
|
||||
}
|
||||
|
||||
// Convert to base64
|
||||
const base64Image = resizedBuffer.toString('base64');
|
||||
|
||||
// Select model
|
||||
const model = args.model || defaultModel || 'anthropic/claude-3.5-sonnet';
|
||||
|
||||
// Prepare message with image
|
||||
const messages = [
|
||||
{
|
||||
role: 'user',
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: args.question || "What's in this image?"
|
||||
},
|
||||
{
|
||||
type: 'image_url',
|
||||
image_url: {
|
||||
url: `data:image/jpeg;base64,${base64Image}`
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
];
|
||||
|
||||
console.error('Sending request to OpenRouter...');
|
||||
|
||||
// Call OpenRouter API
|
||||
const completion = await openai.chat.completions.create({
|
||||
model,
|
||||
messages,
|
||||
});
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: completion.choices[0].message.content || '',
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Error analyzing image:', error);
|
||||
|
||||
if (error instanceof McpError) {
|
||||
throw error;
|
||||
}
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `Error analyzing image: ${error instanceof Error ? error.message : String(error)}`,
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
}
|
||||
135
src/tool-handlers/chat-completion.ts
Normal file
135
src/tool-handlers/chat-completion.ts
Normal file
@@ -0,0 +1,135 @@
|
||||
import OpenAI from 'openai';
|
||||
import { ChatCompletionMessageParam } from 'openai/resources/chat/completions.js';
|
||||
|
||||
// Maximum context tokens
|
||||
const MAX_CONTEXT_TOKENS = 200000;
|
||||
|
||||
export interface ChatCompletionToolRequest {
|
||||
model?: string;
|
||||
messages: ChatCompletionMessageParam[];
|
||||
temperature?: number;
|
||||
}
|
||||
|
||||
// Utility function to estimate token count (simplified)
|
||||
function estimateTokenCount(text: string): number {
|
||||
// Rough approximation: 4 characters per token
|
||||
return Math.ceil(text.length / 4);
|
||||
}
|
||||
|
||||
// Truncate messages to fit within the context window
|
||||
function truncateMessagesToFit(
|
||||
messages: ChatCompletionMessageParam[],
|
||||
maxTokens: number
|
||||
): ChatCompletionMessageParam[] {
|
||||
const truncated: ChatCompletionMessageParam[] = [];
|
||||
let currentTokenCount = 0;
|
||||
|
||||
// Always include system message first if present
|
||||
if (messages[0]?.role === 'system') {
|
||||
truncated.push(messages[0]);
|
||||
currentTokenCount += estimateTokenCount(messages[0].content as string);
|
||||
}
|
||||
|
||||
// Add messages from the end, respecting the token limit
|
||||
for (let i = messages.length - 1; i >= 0; i--) {
|
||||
const message = messages[i];
|
||||
|
||||
// Skip if it's the system message we've already added
|
||||
if (i === 0 && message.role === 'system') continue;
|
||||
|
||||
// For string content, estimate tokens directly
|
||||
if (typeof message.content === 'string') {
|
||||
const messageTokens = estimateTokenCount(message.content);
|
||||
if (currentTokenCount + messageTokens > maxTokens) break;
|
||||
truncated.unshift(message);
|
||||
currentTokenCount += messageTokens;
|
||||
}
|
||||
// For multimodal content (array), estimate tokens for text content
|
||||
else if (Array.isArray(message.content)) {
|
||||
let messageTokens = 0;
|
||||
for (const part of message.content) {
|
||||
if (part.type === 'text' && part.text) {
|
||||
messageTokens += estimateTokenCount(part.text);
|
||||
} else if (part.type === 'image_url') {
|
||||
// Add a token cost estimate for images - this is a simplification
|
||||
// Actual image token costs depend on resolution and model
|
||||
messageTokens += 1000;
|
||||
}
|
||||
}
|
||||
|
||||
if (currentTokenCount + messageTokens > maxTokens) break;
|
||||
truncated.unshift(message);
|
||||
currentTokenCount += messageTokens;
|
||||
}
|
||||
}
|
||||
|
||||
return truncated;
|
||||
}
|
||||
|
||||
export async function handleChatCompletion(
|
||||
request: { params: { arguments: ChatCompletionToolRequest } },
|
||||
openai: OpenAI,
|
||||
defaultModel?: string
|
||||
) {
|
||||
const args = request.params.arguments;
|
||||
|
||||
// Validate model selection
|
||||
const model = args.model || defaultModel;
|
||||
if (!model) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: 'No model specified and no default model configured in MCP settings. Please specify a model or set OPENROUTER_DEFAULT_MODEL in the MCP configuration.',
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
|
||||
// Validate message array
|
||||
if (args.messages.length === 0) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: 'Messages array cannot be empty. At least one message is required.',
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
|
||||
try {
|
||||
// Truncate messages to fit within context window
|
||||
const truncatedMessages = truncateMessagesToFit(args.messages, MAX_CONTEXT_TOKENS);
|
||||
|
||||
const completion = await openai.chat.completions.create({
|
||||
model,
|
||||
messages: truncatedMessages,
|
||||
temperature: args.temperature ?? 1,
|
||||
});
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: completion.choices[0].message.content || '',
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (error) {
|
||||
if (error instanceof Error) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `OpenRouter API error: ${error.message}`,
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
54
src/tool-handlers/get-model-info.ts
Normal file
54
src/tool-handlers/get-model-info.ts
Normal file
@@ -0,0 +1,54 @@
|
||||
import { McpError } from '@modelcontextprotocol/sdk/types.js';
|
||||
import { ModelCache } from '../model-cache.js';
|
||||
|
||||
export interface GetModelInfoToolRequest {
|
||||
model: string;
|
||||
}
|
||||
|
||||
export async function handleGetModelInfo(
|
||||
request: { params: { arguments: GetModelInfoToolRequest } },
|
||||
modelCache: ModelCache
|
||||
) {
|
||||
const args = request.params.arguments;
|
||||
|
||||
try {
|
||||
if (!modelCache.isCacheValid()) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: 'Model cache is empty or expired. Please call search_models first to populate the cache.',
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
|
||||
const model = modelCache.getModel(args.model);
|
||||
if (!model) {
|
||||
throw new McpError('NotFound', `Model '${args.model}' not found`);
|
||||
}
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: JSON.stringify(model, null, 2),
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (error) {
|
||||
if (error instanceof Error) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `Error retrieving model info: ${error.message}`,
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
168
src/tool-handlers/multi-image-analysis.ts
Normal file
168
src/tool-handlers/multi-image-analysis.ts
Normal file
@@ -0,0 +1,168 @@
|
||||
import fetch from 'node-fetch';
|
||||
import sharp from 'sharp';
|
||||
import { McpError } from '@modelcontextprotocol/sdk/types.js';
|
||||
import OpenAI from 'openai';
|
||||
|
||||
export interface MultiImageAnalysisToolRequest {
|
||||
images: Array<{
|
||||
url: string;
|
||||
alt?: string;
|
||||
}>;
|
||||
prompt: string;
|
||||
markdown_response?: boolean;
|
||||
model?: string;
|
||||
}
|
||||
|
||||
async function fetchImageAsBuffer(url: string): Promise<Buffer> {
|
||||
try {
|
||||
// Handle data URLs
|
||||
if (url.startsWith('data:')) {
|
||||
const matches = url.match(/^data:([A-Za-z-+\/]+);base64,(.+)$/);
|
||||
if (!matches || matches.length !== 3) {
|
||||
throw new Error('Invalid data URL');
|
||||
}
|
||||
return Buffer.from(matches[2], 'base64');
|
||||
}
|
||||
|
||||
// Handle file URLs
|
||||
if (url.startsWith('file://')) {
|
||||
const filePath = url.replace('file://', '');
|
||||
const fs = await import('fs/promises');
|
||||
return await fs.readFile(filePath);
|
||||
}
|
||||
|
||||
// Handle http/https URLs
|
||||
const response = await fetch(url);
|
||||
if (!response.ok) {
|
||||
throw new Error(`HTTP error! status: ${response.status}`);
|
||||
}
|
||||
return Buffer.from(await response.arrayBuffer());
|
||||
} catch (error) {
|
||||
console.error(`Error fetching image from ${url}:`, error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
async function processImage(buffer: Buffer): Promise<string> {
|
||||
try {
|
||||
// Get image metadata
|
||||
const metadata = await sharp(buffer).metadata();
|
||||
|
||||
// Calculate dimensions to keep base64 size reasonable
|
||||
const MAX_DIMENSION = 800;
|
||||
const JPEG_QUALITY = 80;
|
||||
|
||||
if (metadata.width && metadata.height) {
|
||||
const largerDimension = Math.max(metadata.width, metadata.height);
|
||||
if (largerDimension > MAX_DIMENSION) {
|
||||
const resizeOptions = metadata.width > metadata.height
|
||||
? { width: MAX_DIMENSION }
|
||||
: { height: MAX_DIMENSION };
|
||||
|
||||
const resizedBuffer = await sharp(buffer)
|
||||
.resize(resizeOptions)
|
||||
.jpeg({ quality: JPEG_QUALITY })
|
||||
.toBuffer();
|
||||
|
||||
return resizedBuffer.toString('base64');
|
||||
}
|
||||
}
|
||||
|
||||
// If no resizing needed, just convert to JPEG
|
||||
const jpegBuffer = await sharp(buffer)
|
||||
.jpeg({ quality: JPEG_QUALITY })
|
||||
.toBuffer();
|
||||
|
||||
return jpegBuffer.toString('base64');
|
||||
} catch (error) {
|
||||
console.error('Error processing image:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
export async function handleMultiImageAnalysis(
|
||||
request: { params: { arguments: MultiImageAnalysisToolRequest } },
|
||||
openai: OpenAI,
|
||||
defaultModel?: string
|
||||
) {
|
||||
const args = request.params.arguments;
|
||||
|
||||
try {
|
||||
// Validate inputs
|
||||
if (!args.images || args.images.length === 0) {
|
||||
throw new McpError('InvalidParams', 'At least one image is required');
|
||||
}
|
||||
|
||||
if (!args.prompt) {
|
||||
throw new McpError('InvalidParams', 'A prompt is required');
|
||||
}
|
||||
|
||||
// Prepare content array for the message
|
||||
const content: Array<any> = [{
|
||||
type: 'text',
|
||||
text: args.prompt
|
||||
}];
|
||||
|
||||
// Process each image
|
||||
for (const image of args.images) {
|
||||
try {
|
||||
// Fetch and process the image
|
||||
const imageBuffer = await fetchImageAsBuffer(image.url);
|
||||
const base64Image = await processImage(imageBuffer);
|
||||
|
||||
// Add to content
|
||||
content.push({
|
||||
type: 'image_url',
|
||||
image_url: {
|
||||
url: `data:image/jpeg;base64,${base64Image}`
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error(`Error processing image ${image.url}:`, error);
|
||||
// Continue with other images if one fails
|
||||
}
|
||||
}
|
||||
|
||||
// If no images were successfully processed
|
||||
if (content.length === 1) {
|
||||
throw new Error('Failed to process any of the provided images');
|
||||
}
|
||||
|
||||
// Select model
|
||||
const model = args.model || defaultModel || 'anthropic/claude-3.5-sonnet';
|
||||
|
||||
// Make the API call
|
||||
const completion = await openai.chat.completions.create({
|
||||
model,
|
||||
messages: [{
|
||||
role: 'user',
|
||||
content
|
||||
}]
|
||||
});
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: completion.choices[0].message.content || '',
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Error in multi-image analysis:', error);
|
||||
|
||||
if (error instanceof McpError) {
|
||||
throw error;
|
||||
}
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `Error analyzing images: ${error instanceof Error ? error.message : String(error)}`,
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
}
|
||||
68
src/tool-handlers/search-models.ts
Normal file
68
src/tool-handlers/search-models.ts
Normal file
@@ -0,0 +1,68 @@
|
||||
import { ModelCache } from '../model-cache.js';
|
||||
import { OpenRouterAPIClient } from '../openrouter-api.js';
|
||||
|
||||
export interface SearchModelsToolRequest {
|
||||
query?: string;
|
||||
provider?: string;
|
||||
minContextLength?: number;
|
||||
maxContextLength?: number;
|
||||
maxPromptPrice?: number;
|
||||
maxCompletionPrice?: number;
|
||||
capabilities?: {
|
||||
functions?: boolean;
|
||||
tools?: boolean;
|
||||
vision?: boolean;
|
||||
json_mode?: boolean;
|
||||
};
|
||||
limit?: number;
|
||||
}
|
||||
|
||||
export async function handleSearchModels(
|
||||
request: { params: { arguments: SearchModelsToolRequest } },
|
||||
apiClient: OpenRouterAPIClient,
|
||||
modelCache: ModelCache
|
||||
) {
|
||||
const args = request.params.arguments;
|
||||
|
||||
try {
|
||||
// Refresh the cache if needed
|
||||
if (!modelCache.isCacheValid()) {
|
||||
const models = await apiClient.getModels();
|
||||
modelCache.setModels(models);
|
||||
}
|
||||
|
||||
// Search models based on criteria
|
||||
const results = modelCache.searchModels({
|
||||
query: args.query,
|
||||
provider: args.provider,
|
||||
minContextLength: args.minContextLength,
|
||||
maxContextLength: args.maxContextLength,
|
||||
maxPromptPrice: args.maxPromptPrice,
|
||||
maxCompletionPrice: args.maxCompletionPrice,
|
||||
capabilities: args.capabilities,
|
||||
limit: args.limit || 10,
|
||||
});
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: JSON.stringify(results, null, 2),
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (error) {
|
||||
if (error instanceof Error) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `Error searching models: ${error.message}`,
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
50
src/tool-handlers/validate-model.ts
Normal file
50
src/tool-handlers/validate-model.ts
Normal file
@@ -0,0 +1,50 @@
|
||||
import { ModelCache } from '../model-cache.js';
|
||||
|
||||
export interface ValidateModelToolRequest {
|
||||
model: string;
|
||||
}
|
||||
|
||||
export async function handleValidateModel(
|
||||
request: { params: { arguments: ValidateModelToolRequest } },
|
||||
modelCache: ModelCache
|
||||
) {
|
||||
const args = request.params.arguments;
|
||||
|
||||
try {
|
||||
if (!modelCache.isCacheValid()) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: 'Model cache is empty or expired. Please call search_models first to populate the cache.',
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
|
||||
const isValid = modelCache.hasModel(args.model);
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: JSON.stringify({ valid: isValid }),
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (error) {
|
||||
if (error instanceof Error) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `Error validating model: ${error.message}`,
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
14
tsconfig.json
Normal file
14
tsconfig.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"target": "es2022",
|
||||
"module": "NodeNext",
|
||||
"moduleResolution": "NodeNext",
|
||||
"esModuleInterop": true,
|
||||
"strict": true,
|
||||
"outDir": "dist",
|
||||
"declaration": true,
|
||||
"sourceMap": true,
|
||||
"skipLibCheck": true
|
||||
},
|
||||
"include": ["src/**/*"]
|
||||
}
|
||||
Reference in New Issue
Block a user