Robinhood
The Robinhood MCP Server allows your AI assistants and MCP clients to interact with the Robinhood Crypto API.
It handles authentication, account management, market data retrieval, and trading operations through both REST API and WebSocket interfaces.
Featuress
- 🔒 Secure authentication and account management
- 💹 Real-time market data via WebSocket
- 📊 REST API for trading operations and account info
- ⚡ WebSocket support for order updates
- 🛡️ Rate limiting and error handling
- 🔧 Easy configuration with environment variables
Use Cases
- Cryptocurrency trading bots can use this server to automate trading strategies on Robinhood
- Portfolio management applications can integrate with Robinhood to provide users with a unified view of their crypto holdings
- Market analysis tools can leverage the real-time data to provide insights and visualizations
- Personal finance apps can use the account management features to include Robinhood crypto balances in overall net worth calculations
How to Use It
- Clone the repository:
git clone https://github.com/rohitsingh-iitd/robinhood-mcp-server
cd robinhood-mcp-server- Set up a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: .\venv\Scripts\activate- Install dependencies:
pip install -r requirements.txt- Create a
.envfile with your Robinhood API credentials:
ROBINHOOD_API_KEY=your_api_key_here
ROBINHOOD_PRIVATE_KEY=your_base64_encoded_private_key_here
# Optional (with defaults)
HOST=0.0.0.0
PORT=8000
WEBSOCKET_PORT=8001
DEBUG=False
LOG_LEVEL=INFO
LOG_FILE=robinhood_mcp_server.log
RATE_LIMIT_ENABLED=True
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_PERIOD=60- Start the server:
python -m src.mainThe server will start the REST API on port 8000 and WebSocket on port 8001 by default.
Key API Endpoints:
- Authentication:
GET /auth/status - Account Info:
GET /account - Market Data:
GET /market/best-price - Trading:
POST /trading/orders
WebSocket API:
- Connect to
ws://localhost:8001 - Subscribe to market data: Send
{"type": "market_data", "action": "subscribe", "symbols": ["BTC-USD"]} - Subscribe to order updates: Send
{"type": "orders", "action": "subscribe"}
FAQs
Q: How do I obtain Robinhood API credentials?
A: You’ll need to apply for API access through Robinhood’s developer program. Once approved, you’ll receive an API key and instructions to generate your private key.
Q: Can I use this server with a paper trading account?
A: Yes, you can use it with a paper trading account by using the appropriate API credentials for your paper trading environment.
Q: How can I handle rate limiting?
A: The server implements rate limiting by default. You can adjust the settings in the .env file using the RATE_LIMIT_* variables.
Q: Is this server suitable for high-frequency trading?
A: While it can handle real-time data and order placement, it’s not optimized for high-frequency trading. For HFT, you might need to implement additional optimizations.
Q: How can I secure my API credentials when deploying this server?
A: Always use environment variables or a secure secrets management system to store your API credentials. Never hard-code them or include them in version control.
Latest MCP Servers
Notion
Log Mcp
Apple
Featured MCP Servers
Notion
Claude Peers
Excalidraw
FAQs
Q: What exactly is the Model Context Protocol (MCP)?
A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.
Q: How is MCP different from OpenAI's function calling or plugins?
A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.
Q: Can I use MCP with frameworks like LangChain?
A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.
Q: Why was MCP created? What problem does it solve?
A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.
Q: Is MCP secure? What are the main risks?
A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.
Q: Who is behind MCP?
A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.



