fschaeffler/quant-connect-mcp-lambda

QuantConnect MCP Server

Model Context Protocol server for AI assistants to interact with QuantConnect's algorithmic trading platform.

Overview

Bridge between AI assistants and QuantConnect platform for:

• Algorithm development and management
• Backtesting and live trading deployment
• Data management and research tools
• Project collaboration and optimization

Architecture

The project follows a monorepo structure with multiple specialized packages:

aws-lambda-mcp/
├── libs/                           # Core libraries
│   ├── quant-connect-mcp/         # Main MCP server implementation (67 tools)
│   ├── quant-connect-types/       # TypeScript types from QuantConnect API
│   ├── quant-connect-client/      # HTTP client for QuantConnect API
│   └── middy/                     # AWS Lambda middleware integration
├── services/                      # Deployable services
│   ├── quant-connect/            # Main Lambda service
│   └── quant-connect-local/      # Local development service
├── infra/                        # AWS CDK infrastructure
└── e2e/                          # End-to-end testing

Core Components

• MCP Server: Model Context Protocol implementation with 67 tools organized by category
• QuantConnect Client: HTTP client with authentication, rate limiting, and error handling
• AWS Lambda: Serverless deployment with API Gateway, Secrets Manager integration
• Type Safety: Auto-generated TypeScript types with Zod validation
• Local Development: HTTP server for development without AWS infrastructure

Note: Uses modified OpenAPI spec to fix validation issues in the official specification.

vs Official QuantConnect MCP Server

Alternative to official MCP server with key differences:

Architecture

Tools

Key Differences

Missing Features:

• patch_file tool (git-style patches)
• authorize_connection tool (two-step live auth)

Unique Features:

• AWS cloud integration (Lambda, API Gateway, Secrets Manager, CloudWatch)
• Infrastructure as Code (CDK)
• Full TypeScript type safety with Zod validation
• Local development server
• Built-in monitoring and authentication

Development

Production

Features

67 tools organized across categories:

Project Management (5 tools)

• Create, read, update, delete projects
• List user projects

File Operations (5 tools)

• Create, read, update, delete algorithm files
• Update file names and contents

Backtesting (10 tools)

• Create and manage backtests
• Read results, charts, orders, insights, reports
• Historical simulation analytics

Live Trading (12 tools)

• Deploy algorithms to live trading
• Monitor portfolio, orders, logs
• Risk controls and liquidation

AI-Powered Tools (6 tools)

• Code completion and syntax checking
• Error message enhancement
• PEP8 formatting
• QuantConnect documentation search

Research & Data (8 tools)

• Object store for research data
• Parameter optimization
• Compilation services
• Account information

Collaboration & Infrastructure (21 tools)

• Project collaboration tools
• Node management
• Live command broadcasting
• Health monitoring
• Version information

Quick Start

Prerequisites: Node.js 18+, QuantConnect account

Local Development

# Install dependencies
git clone <repository-url>
cd aws-lambda-mcp
npm ci
npm run build

# Configure credentials
cd services/quant-connect-local
echo "QUANTCONNECT_USER_ID=your-user-id" > .env
echo "QUANTCONNECT_API_TOKEN=your-api-token" >> .env

# Start development server
npm start
# Server runs at http://localhost:55555/mcp

# Test the connection
curl -X POST http://localhost:55555/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

AWS Production Deployment

# Deploy infrastructure (creates empty secrets)
cd infra && npm run deploy

# Populate secrets with actual QuantConnect credentials
aws secretsmanager update-secret \
  --secret-id "quant-connect-mcp/user-id" \
  --secret-string "your-quantconnect-user-id"
aws secretsmanager update-secret \
  --secret-id "quant-connect-mcp/api-token" \
  --secret-string "your-quantconnect-api-token"

After deployment, you'll receive API Gateway endpoint and API key for authentication.

Configuration

Local Development

• Environment Variables: QUANTCONNECT_USER_ID, QUANTCONNECT_USER_ID in .env file
• Server: Runs on port 55555 (configurable via PORT)
• Debug: Enable with DEBUG=* or DEBUG=quant-connect:*

AWS Production

• API Gateway: REST endpoint with API key authentication
• Lambda Function: Node.js 22.x runtime, 1024MB memory, 30s timeout
• Secrets Manager: Encrypted credential storage
• CloudWatch: Logging and monitoring with 3-day retention

MCP Protocol Support

Enhanced MCP protocol implementation:

• Full JSON-RPC 2.0 compliance with proper request/response handling
• Notification support for MCP client handshake (initialize/initialized flow)
• HTTP transport compatibility with AWS Lambda/API Gateway
• Error handling with appropriate HTTP status codes
• 67 tools with comprehensive input/output validation using Zod schemas

AWS Infrastructure

Components

• API Gateway: REST API with throttling (100 req/sec, 200 burst)
• Lambda Function: Serverless execution with automatic scaling
• Secrets Manager: Secure credential storage with rotation support
• CloudWatch: Structured logging and performance metrics
• IAM Roles: Minimal permissions (secrets access, logging)

Security Features

• API key authentication for all requests
• HTTPS/TLS encryption in transit
• Encrypted secrets and logs at rest
• Zod input validation for all tools
• No sensitive data in logs or responses

Scaling & Performance

• Automatic scaling: Up to 1000 concurrent Lambda executions
• Cold start: ~2-3 seconds initial, ~500ms subsequent
• Typical response: 1-5 seconds
• Memory usage: 150-300MB typical, up to 500MB for large operations

Development

Monorepo Structure

npm run lint        # Code linting across all packages
npm run build       # Build all packages  
npm test           # Run unit tests
npm run test:e2e   # End-to-end tests (requires deployed server)

Hot Reloading for Development

Enable file watching during development:

# Install nodemon globally
npm install -g nodemon

# Start with automatic restart on changes
cd services/quant-connect-local
nodemon --watch src --ext ts,js --exec "npm start"

Testing Strategy

• Unit Tests: 100% coverage for all TypeScript files
• Integration Tests: Component interaction validation
• E2E Tests: Real server testing with AWS authentication

# Run specific test suites
npm test                    # All unit tests
npm run test:coverage      # With coverage report
cd e2e && npm run test:e2e # End-to-end tests

E2E tests validate:

• API Gateway authentication with API keys
• Complete MCP protocol compliance
• All 67 tools functionality
• Real QuantConnect API integration

Type Safety & Validation

Generated Types

• Source: Auto-generated from QuantConnect OpenAPI 2.0.0 specification
• Validation: Zod schemas for runtime validation
• Features: Automatic date parsing, null safety, coercion

Tool Validation

All tools include:

• Input validation: Zod schema validation for parameters
• Output validation: Structured response validation
• Error handling: Comprehensive error mapping
• Annotations: Tool behavior hints (readOnly, destructive, idempotent)

Documentation

Complete Documentation Guide

• - Complete MCP tools and API documentation
• - Development setup, debugging, and best practices
• - How to contribute, coding standards, and release process

Architecture Documentation

Each component includes comprehensive documentation covering setup, configuration, usage examples, troubleshooting, and best practices.

Cost Optimization

AWS Pricing (Estimated)

• Lambda: ~$0.0005 per invocation (1024MB, 30s avg)
• API Gateway: $3.50 per million API calls
• Secrets Manager: $0.40 per secret per month
• CloudWatch: $0.50 per GB logs ingested

Optimization Strategies

• Right-size Lambda memory allocation
• Implement request caching for repeated operations
• Use provisioned concurrency for predictable workloads
• Monitor and optimize based on CloudWatch metrics

Security

• AWS Secrets Manager: Encrypted credential storage with automatic rotation
• API Gateway: Authentication, rate limiting, and request validation
• Zod Validation: All inputs validated against TypeScript schemas
• IAM Permissions: Minimal required permissions (secrets read, logging)
• TLS Encryption: All data encrypted in transit
• Audit Logging: All requests logged to CloudWatch

Troubleshooting

Common Issues

1. Authentication Errors

   • Verify QuantConnect credentials are correct
   • Check AWS Secrets Manager configuration (production)
   • Ensure environment variables are set (local development)

2. Cold Start Timeouts

   • First Lambda invocation may take 2-3 seconds
   • Consider provisioned concurrency for production workloads

3. Rate Limiting

   • API Gateway enforces 100 req/sec by default
   • Implement exponential backoff for high-volume usage

4. Tool Validation Errors

   • Check input parameters match expected Zod schemas
   • Review tool documentation for required fields

Debug Mode

Enable verbose logging:

# Local development
DEBUG=* npm start

# Lambda (via environment variables)
LOG_LEVEL=debug

Acknowledgments

This project's Middy MCP integration is inspired by the middy-mcp package by Frédéric Barthelet. While our implementation is independently developed with different architectural choices, we acknowledge the foundational work done by the middy-mcp community in establishing patterns for MCP server integration with AWS Lambda.

License

This project is licensed under the MIT License - see the file for details.

Support

• QuantConnect Documentation
• MCP Specification
• Report issues via GitHub Issues