IBKR TWS MCP Server
MCP server for Interactive Brokers TWS API, enabling automated financial workflows like portfolio rebalancing through 51+ tools for market data, account management, and order execution, plus real-time WebSocket streaming.
README
IBKR TWS MCP Server
This project implements a Model Context Protocol (MCP) server for the Interactive Brokers (IBKR) Trader Workstation (TWS) API. It uses the official modelcontextprotocol/python-sdk and ib_async to expose key TWS functionalities as MCP tools, enabling seamless integration with LLM clients for automated financial workflows, such as portfolio rebalancing.
The server supports HTTP Streaming for MCP protocol and WebSocket streaming for real-time data subscriptions (market data, portfolio updates, news bulletins).
Features
- MCP Compliance: Built with FastMCP for full adherence to the Model Context Protocol with HTTP streaming support.
- Asynchronous TWS Integration: Leverages
ib_async(maintained fork of ib-insync) for non-blocking, asynchronous interaction with the TWS API. - Comprehensive Toolset: 51+ tools for connection management, market data retrieval (historical and streaming), account and portfolio querying, and order management (placing and canceling orders).
- MCP Prompts: 6 guided workflows that combine multiple tools into expert-level trading strategies (bracket orders, portfolio rebalancing, risk assessment, options strategies, market analysis, and workspace setup).
- Real-time WebSocket Streaming: Three dedicated WebSocket endpoints for continuous real-time data:
- Market Data Stream - Real-time quotes for multiple symbols
- Portfolio Stream - Live portfolio and account updates
- News Stream - IBKR news bulletins and system messages
- HTTP Streaming Transport: Uses chunked transfer encoding for efficient MCP tool responses.
- CORS Support: Configured to allow cross-origin requests from browser-based MCP clients (e.g., Goflow, web applications).
- Containerized Deployment: Ready-to-use Docker and Docker Compose configuration.
Architecture
┌─────────────────────────────────────────────────┐
│ Client Application │
├─────────────────────────────────────────────────┤
│ 1. MCP Client (HTTP Streaming) │
│ POST /api/v1/mcp │
│ - Tool calls: connect, orders, positions │
│ │
│ 2. WebSocket Clients (real-time streams) │
│ WS /api/v1/stream/market-data │
│ WS /api/v1/stream/portfolio │
│ WS /api/v1/stream/news │
└─────────────────────────────────────────────────┘
│ │
│ HTTP POST │ WebSocket
▼ ▼
┌─────────────────────────────────────────────────┐
│ IBKR TWS MCP Server │
├─────────────────────────────────────────────────┤
│ • FastMCP Server (HTTP streaming) │
│ • WebSocket Handlers (real-time streaming) │
│ • Shared TWS Client (ib_async) │
└─────────────────────────────────────────────────┘
│
│ IB Client Protocol
▼
┌─────────────────────────────────────────────────┐
│ TWS / IB Gateway │
│ (127.0.0.1:7497 or :4001) │
└─────────────────────────────────────────────────┘
Quick Start
1. Install Dependencies
# Using uv (recommended)
uv sync
# Or using pip
pip install -r requirements.txt
2. Configure Environment
# Copy example environment file
cp .env.example .env
# Edit .env with your settings
TWS_HOST=127.0.0.1
TWS_PORT=7497
TWS_CLIENT_ID=1
TWS_PAPER_ACCOUNT=DU2515295
SERVER_HOST=0.0.0.0
SERVER_PORT=8000
3. CORS Configuration (Browser Clients)
The server is configured to allow cross-origin requests from browser-based MCP clients:
- Allowed Origins: All origins (
*) - supports external domains likehttps://test.lizhao.net - Allowed Methods: GET, POST, PUT, DELETE, OPTIONS, HEAD
- Allowed Headers: All headers including MCP-specific headers (
Mcp-Session-Id,Mcp-Initialize-Request) - Credentials: Enabled for authenticated requests
- Preflight Cache: 24 hours
This enables integration with browser-based MCP clients like Goflow without CORS restrictions.
4. Start TWS/Gateway
Launch Interactive Brokers TWS or IB Gateway and enable API connections.
5. Run the Server
uv run python main.py
5. Test the Server
# Health check
curl http://localhost:8000/health
# MCP tool call (connect to TWS)
curl -X POST http://localhost:8000/api/v1/mcp \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "ibkr_connect",
"arguments": {"host": "127.0.0.1", "port": 7497, "clientId": 1}
},
"id": 1
}'
WebSocket Streaming Examples
Market Data Stream (Python)
import websockets
import json
import asyncio
async def stream_quotes():
async with websockets.connect('ws://localhost:8000/api/v1/stream/market-data') as ws:
# Subscribe to AAPL
await ws.send(json.dumps({
"action": "subscribe",
"symbol": "AAPL",
"secType": "STK",
"exchange": "SMART",
"currency": "USD"
}))
# Receive real-time updates
async for message in ws:
data = json.loads(message)
if data["type"] == "market_data":
print(f"{data['symbol']}: ${data['data']['last']}")
asyncio.run(stream_quotes())
Market Data Stream (JavaScript/Browser)
const ws = new WebSocket('ws://localhost:8000/api/v1/stream/market-data');
ws.onopen = () => {
ws.send(JSON.stringify({
action: 'subscribe',
symbol: 'AAPL',
secType: 'STK'
}));
};
ws.onmessage = (event) => {
const data = JSON.parse(event.data);
if (data.type === 'market_data') {
console.log(`${data.symbol}: $${data.data.last}`);
}
};
For complete examples including portfolio streams and news bulletins, see HTTP Streaming Examples.
MCP Prompts
The server includes 6 comprehensive prompts that provide guided workflows for complex trading operations:
Portfolio Management
- setup_trading_workspace - Complete workspace setup with streaming resources
- rebalance_portfolio - Portfolio rebalancing with tax optimization and OCA groups
- assess_portfolio_risk - Risk analysis with beta weighting, VaR, and stress testing
Trading Execution
- execute_bracket_order - Bracket orders with take-profit and stop-loss automation
- execute_options_strategy - Options strategies (covered calls, protective puts, collars, etc.)
Market Analysis
- analyze_market_conditions - Multi-dimensional analysis combining technicals, fundamentals, news, and options
Each prompt provides step-by-step workflows with tool call examples, best practices, risk warnings, and sample calculations. See Prompts Guide for details.
Getting Started
Please refer to the Setup Guide for detailed instructions on prerequisites, environment configuration, and running the server locally or in a container.
API Reference
A complete list of all exposed MCP tools, their parameters, and return types can be found in the API Reference.
Streaming Documentation
- HTTP Streaming Migration - Migration plan and architecture
- HTTP Streaming Examples - Complete client examples
- HTTP Streaming Summary - Changes and best practices
End-to-End Testing
The server is designed to support the portfolio rebalancing E2E case. You can test all functionalities using the Claude MCP Inspector.
See the dedicated section in the API Reference for a step-by-step guide on how to use the Inspector to interact with your running server.
Project Structure
For a detailed explanation of the project organization, see PROJECT_STRUCTURE.md.
ibkr-tws-mcp-server/
├── src/ # Source code
│ ├── server.py # FastMCP server and tool definitions
│ ├── tws_client.py # TWS client wrapper using ib_async
│ ├── models.py # Pydantic data models
│ └── streaming/ # WebSocket streaming handlers
│ ├── websocket_manager.py
│ ├── market_data.py
│ ├── portfolio.py
│ └── news.py
├── tests/ # Test suite
│ ├── unit/ # Unit tests with mocks
│ └── integration/ # Integration test documentation
├── docs/ # Documentation
│ ├── API.md # MCP tools API reference
│ ├── SETUP.md # Setup and deployment guide
│ ├── HTTP_STREAMING_MIGRATION.md # Streaming migration plan
│ ├── HTTP_STREAMING_EXAMPLES.md # Streaming examples
│ ├── HTTP_STREAMING_SUMMARY.md # Streaming summary
│ └── ... # Additional guides and troubleshooting
├── diagnostics/ # Diagnostic and testing scripts
├── scripts/ # Utility scripts
├── main.py # Application entry point
├── pyproject.toml # Project dependencies
└── README.md # This file
Documentation
- Setup Guide - Installation and configuration
- API Reference - Complete tool documentation
- Prompts Guide - MCP prompts quick reference
- Design Document - Architecture and design decisions
- HTTP Streaming Guide - Real-time streaming architecture
- Streaming Examples - Client code examples
- Project Structure - Detailed project organization
- Migration Guide - ib-insync to ib_async migration
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.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
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.