# Claude Code AI Collaboration MCP Server
A powerful Model Context Protocol (MCP) server that enables AI collaboration through multiple providers with advanced strategies and comprehensive tooling.
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[](https://opensource.org/licenses/MIT)
[](https://nodejs.org/)
## 🌟 Features
### 🤖 Multi-Provider AI Integration
- **DeepSeek**: Primary provider with optimized performance
- **OpenAI**: GPT models integration
- **Anthropic**: Claude models support
- **O3**: Next-generation model support
### 🚀 Advanced Collaboration Strategies
- **Parallel**: Execute requests across multiple providers simultaneously
- **Sequential**: Chain provider responses for iterative improvement
- **Consensus**: Build agreement through multiple provider opinions
- **Iterative**: Refine responses through multiple rounds
### 🛠️ Comprehensive MCP Tools
- **collaborate**: Multi-provider collaboration with strategy selection
- **review**: Content analysis and quality assessment
- **compare**: Side-by-side comparison of multiple items
- **refine**: Iterative content improvement
### 📊 Enterprise Features
- **Caching**: Memory and Redis-compatible caching system
- **Metrics**: OpenTelemetry-compatible performance monitoring
- **Search**: Full-text search with inverted indexing
- **Synthesis**: Intelligent response aggregation
## 🚀 Quick Start
> **📖 New to MCP?** Check out our [Quick Start Guide](docs/QUICKSTART.md) for a 5-minute setup!
### Prerequisites
- Node.js 18.0.0 or higher
- pnpm 8.0.0 or higher
- TypeScript 5.3.0 or higher
### Installation
```bash
# Clone the repository
git clone https://github.com/atsuki-sakai/ai_collaboration_mcp_server.git
cd ai_collaboration_mcp_server
# Install dependencies
pnpm install
# Build the project
pnpm run build
# Run tests
pnpm test
```
### Configuration
1. **Environment Variables**:
```bash
# Required: Set your API keys
export DEEPSEEK_API_KEY="your-deepseek-api-key"
export OPENAI_API_KEY="your-openai-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
# Optional: Configure other settings
export MCP_DEFAULT_PROVIDER="deepseek"
export MCP_PROTOCOL="stdio"
```
2. **Configuration Files**:
- `config/default.yaml`: Default configuration
- `config/development.yaml`: Development settings
- `config/production.yaml`: Production settings
### Running the Server
```bash
# Start with default settings
pnpm start
# Start with specific protocol
node dist/index.js --protocol stdio
# Start with custom providers
node dist/index.js --providers deepseek,openai --default-provider deepseek
# Enable debug mode
NODE_ENV=development LOG_LEVEL=debug pnpm start
```
## 🔗 Claude Code Integration
### Connecting to Claude Code
To use this MCP server with Claude Code, you need to configure Claude Code to recognize and connect to your server.
#### 1. Automated Setup (Recommended)
Use the automated setup script for easy configuration:
```bash
# Navigate to your project directory
cd /Users/atsukisakai/Desktop/ai_collaboration_mcp_server
# Run automated setup with your DeepSeek API key
./scripts/setup-claude-code.sh --api-key "your-deepseek-api-key"
# Or with multiple providers
./scripts/setup-claude-code.sh \
--api-key "your-deepseek-key" \
--openai-key "your-openai-key" \
--anthropic-key "your-anthropic-key"
# Alternative using pnpm
pnpm run setup:claude-code -- --api-key "your-deepseek-key"
```
The setup script will:
- ✅ Build the MCP server
- ✅ Create Claude Code configuration file
- ✅ Test the server connection
- ✅ Provide next steps
#### 1b. Manual Setup
If you prefer manual setup:
```bash
# Navigate to your project directory
cd /Users/atsukisakai/Desktop/ai_collaboration_mcp_server
# Install dependencies and build
pnpm install
pnpm run build
# Set your DeepSeek API key
export DEEPSEEK_API_KEY="your-deepseek-api-key"
# Test the server
pnpm run verify-deepseek
```
#### 2. Configure Claude Code
Create or update the Claude Code configuration file:
**Note**: There are two server options:
- `simple-server.js` - Simple implementation with DeepSeek only (recommended for testing)
- `index.js` - Full implementation with all providers and features
**macOS/Linux:**
```bash
# Create config directory if it doesn't exist
mkdir -p ~/.config/claude-code
# Create configuration file (simple server - recommended for testing)
cat > ~/.config/claude-code/claude_desktop_config.json << 'EOF'
{
"mcpServers": {
"ai-collaboration": {
"command": "node",
"args": ["/Users/atsukisakai/Desktop/ai_collaboration_mcp_server/dist/simple-server.js"],
"env": {
"DEEPSEEK_API_KEY": "your-deepseek-api-key"
}
}
}
}
EOF
# Or use the full server for all features
# Replace simple-server.js with index.js in the args above
```
**Windows:**
```cmd
# Create config directory
mkdir "%APPDATA%\Claude"
# Create configuration file (use your preferred text editor)
# File: %APPDATA%\Claude\claude_desktop_config.json
```
#### 3. Configuration Options
```json
{
"mcpServers": {
"ai-collaboration": {
"command": "node",
"args": [
"/Users/atsukisakai/Desktop/ai_collaboration_mcp_server/dist/index.js",
"--default-provider", "deepseek",
"--providers", "deepseek,openai"
],
"env": {
"DEEPSEEK_API_KEY": "your-deepseek-api-key",
"OPENAI_API_KEY": "your-openai-api-key",
"ANTHROPIC_API_KEY": "your-anthropic-api-key",
"NODE_ENV": "production",
"LOG_LEVEL": "info",
"MCP_DISABLE_CACHING": "false",
"MCP_DISABLE_METRICS": "false"
}
}
}
}
```
#### 4. Available Tools in Claude Code
After restarting Claude Code, you'll have access to these powerful tools:
- **🤝 collaborate** - Multi-provider AI collaboration
- **📝 review** - Content analysis and quality assessment
- **⚖️ compare** - Side-by-side comparison of multiple items
- **✨ refine** - Iterative content improvement
#### 5. Usage Examples in Claude Code
```
# Use DeepSeek for code explanation
Please use the collaborate tool to explain this Python code with DeepSeek
# Review code quality
Use the review tool to analyze the quality of this code
# Compare multiple solutions
Use the compare tool to compare these 3 approaches to solving this problem
# Improve code iteratively
Use the refine tool to make this function more efficient
```
#### 6. Troubleshooting
**Check MCP server connectivity:**
```bash
# Test if the server starts correctly
DEEPSEEK_API_KEY="your-key" node dist/index.js --help
```
**View logs:**
```bash
# Check application logs
tail -f logs/application-$(date +%Y-%m-%d).log
```
**Verify Claude Code configuration:**
1. Restart Claude Code completely
2. In a new conversation, ask "What tools are available?"
3. You should see the four MCP tools listed
4. Test with a simple command like "Use collaborate to say hello"
#### 7. Configuration File Locations
- **macOS**: `~/.config/claude-code/claude_desktop_config.json`
- **Windows**: `%APPDATA%\Claude\claude_desktop_config.json`
- **Linux**: `~/.config/claude-code/claude_desktop_config.json`
## 📖 Usage
### MCP Tools
#### Collaborate Tool
Execute multi-provider collaboration with strategy selection:
```json
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "collaborate",
"arguments": {
"prompt": "Explain quantum computing in simple terms",
"strategy": "consensus",
"providers": ["deepseek", "openai"],
"config": {
"timeout": 30000,
"consensus_threshold": 0.7
}
}
}
}
```
#### Review Tool
Analyze content quality and provide detailed feedback:
```json
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "review",
"arguments": {
"content": "Your content here...",
"criteria": ["accuracy", "clarity", "completeness"],
"review_type": "comprehensive"
}
}
}
```
#### Compare Tool
Compare multiple items with detailed analysis:
```json
{
"jsonrpc": "2.0",
"id": 3,
"method": "tools/call",
"params": {
"name": "compare",
"arguments": {
"items": [
{"id": "1", "content": "Option A"},
{"id": "2", "content": "Option B"}
],
"comparison_dimensions": ["quality", "relevance", "innovation"]
}
}
}
```
#### Refine Tool
Iteratively improve content quality:
```json
{
"jsonrpc": "2.0",
"id": 4,
"method": "tools/call",
"params": {
"name": "refine",
"arguments": {
"content": "Content to improve...",
"refinement_goals": {
"primary_goal": "clarity",
"target_audience": "general public"
}
}
}
}
```
### Available Resources
- **collaboration_history**: Access past collaboration results
- **provider_stats**: Monitor provider performance metrics
- **tool_usage**: Track tool utilization statistics
## 🏗️ Architecture
### Core Components
```
src/
├── core/ # Core framework components
│ ├── types.ts # Dependency injection symbols
│ ├── logger.ts # Structured logging
│ ├── config.ts # Configuration management
│ ├── container.ts # DI container setup
│ ├── provider-manager.ts # AI provider orchestration
│ ├── strategy-manager.ts # Execution strategy management
│ └── tool-manager.ts # MCP tool management
├── providers/ # AI provider implementations
│ ├── base-provider.ts # Common provider functionality
│ ├── deepseek-provider.ts
│ ├── openai-provider.ts
│ ├── anthropic-provider.ts
│ └── o3-provider.ts
├── strategies/ # Collaboration strategies
│ ├── parallel-strategy.ts
│ ├── sequential-strategy.ts
│ ├── consensus-strategy.ts
│ └── iterative-strategy.ts
├── tools/ # MCP tool implementations
│ ├── collaborate-tool.ts
│ ├── review-tool.ts
│ ├── compare-tool.ts
│ └── refine-tool.ts
├── services/ # Enterprise services
│ ├── cache-service.ts
│ ├── metrics-service.ts
│ ├── search-service.ts
│ └── synthesis-service.ts
├── server/ # MCP server implementation
│ └── mcp-server.ts
└── types/ # Type definitions
├── common.ts
├── interfaces.ts
└── index.ts
```
### Design Principles
- **Dependency Injection**: Clean architecture with InversifyJS
- **Strategy Pattern**: Pluggable collaboration strategies
- **Provider Abstraction**: Unified interface for different AI services
- **Performance**: Efficient caching and rate limiting
- **Observability**: Comprehensive metrics and logging
- **Extensibility**: Easy to add new providers and strategies
## 🔧 Configuration
### Configuration Schema
The server uses YAML configuration files with JSON Schema validation. See `config/schema.json` for the complete schema.
### Key Configuration Sections
- **Server**: Basic server settings (name, version, protocol)
- **Providers**: AI provider configurations and credentials
- **Strategies**: Strategy-specific settings and timeouts
- **Cache**: Caching behavior (memory, Redis, file)
- **Metrics**: Performance monitoring settings
- **Logging**: Log levels and output configuration
### Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `DEEPSEEK_API_KEY` | DeepSeek API key | Required |
| `OPENAI_API_KEY` | OpenAI API key | Optional |
| `ANTHROPIC_API_KEY` | Anthropic API key | Optional |
| `O3_API_KEY` | O3 API key (defaults to OPENAI_API_KEY) | Optional |
| `MCP_PROTOCOL` | Transport protocol | `stdio` |
| `MCP_DEFAULT_PROVIDER` | Default AI provider | `deepseek` |
| `NODE_ENV` | Environment mode | `production` |
| `LOG_LEVEL` | Logging level | `info` |
## 📊 Monitoring & Metrics
### Built-in Metrics
- **Request Metrics**: Response times, success rates, error counts
- **Provider Metrics**: Individual provider performance
- **Tool Metrics**: Usage statistics per MCP tool
- **Cache Metrics**: Hit rates, memory usage
- **System Metrics**: CPU, memory, and resource utilization
### OpenTelemetry Integration
The server supports OpenTelemetry for distributed tracing and metrics collection:
```yaml
metrics:
enabled: true
export:
enabled: true
format: "opentelemetry"
endpoint: "http://localhost:4317"
```
## 🧪 Testing
### Test Coverage
- **Unit Tests**: 95+ individual component tests
- **Integration Tests**: End-to-end MCP protocol testing
- **E2E Tests**: Complete workflow validation
- **API Tests**: Direct provider API validation
### Running Tests
```bash
# Run all tests
pnpm test
# Run with coverage
pnpm run test:coverage
# Run specific test suites
pnpm run test:unit
pnpm run test:integration
pnpm run test:e2e
# Verify API connectivity
pnpm run verify-deepseek
```
## 🚢 Deployment
### Docker
```dockerfile
# Build image
docker build -t claude-code-ai-collab-mcp .
# Run container
docker run -d \
-e DEEPSEEK_API_KEY=your-key \
-p 3000:3000 \
claude-code-ai-collab-mcp
```
### Production Considerations
- **Load Balancing**: Multiple server instances for high availability
- **Caching**: Redis for distributed caching
- **Monitoring**: Prometheus/Grafana for metrics visualization
- **Security**: API key rotation and rate limiting
- **Backup**: Regular configuration and data backups
## 🤝 Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
### Development Setup
```bash
# Fork and clone the repository
git clone https://github.com/atsuki-sakai/ai_collaboration_mcp_server.git
cd ai_collaboration_mcp_server
# Install dependencies
pnpm install
# Start development
pnpm run dev
# Run tests
pnpm test
# Lint and format
pnpm run lint
pnpm run lint:fix
```
## 📋 Roadmap
### Version 1.1
- [ ] GraphQL API support
- [ ] WebSocket transport protocol
- [ ] Advanced caching strategies
- [ ] Custom strategy plugins
### Version 1.2
- [ ] Multi-tenant support
- [ ] Enhanced security features
- [ ] Performance optimizations
- [ ] Additional AI providers
### Version 2.0
- [ ] Distributed architecture
- [ ] Advanced workflow orchestration
- [ ] Machine learning optimization
- [ ] Enterprise SSO integration
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🆘 Support
- **Documentation**: [Wiki](https://github.com/atsuki-sakai/ai_collaboration_mcp_server/wiki)
- **Issues**: [GitHub Issues](https://github.com/atsuki-sakai/ai_collaboration_mcp_server/issues)
- **Discussions**: [GitHub Discussions](https://github.com/atsuki-sakai/ai_collaboration_mcp_server/discussions)
- **Email**: support@claude-code-ai-collab.com
## 🙏 Acknowledgments
- [Model Context Protocol](https://modelcontextprotocol.io/) for the foundational protocol
- [InversifyJS](https://inversify.io/) for dependency injection
- [TypeScript](https://www.typescriptlang.org/) for type safety
- All AI provider APIs for enabling collaboration
---
**Built with ❤️ by the Claude Code AI Collaboration Team**# think_hub
