Zerodha MCP Server
Enables trading operations on Zerodha platform through natural language, supporting account management, order placement/modification, portfolio holdings, positions, margins, and stock news retrieval.
README
Zerodha MCP Server & Client
A Python-based trading assistant that connects to a Zerodha MCP server to help users manage their trading account.
Features
- Account Management: Manage Zerodha trading account, orders, and positions
- Interactive Chat Interface: Natural language interface for trading operations
- MCP Integration: Built on the Model Context Protocol for standardized communication
- Zerodha API Integration: Uses Zerodha's API to interact with the trading platform
- Google ADK Agent: Uses Google ADK Agent to interact with the trading platform
Tech Stack
- Protocol: Model Context Protocol (MCP)
- Agent Framework:
Tools
- Place Orders: Place orders in the trading platform
- Modify Orders: Modify orders in the trading platform
- Cancel Orders: Cancel orders in the trading platform
- Get Orders: Get orders in the trading platform
- Get Order History: Get order history in the trading platform
- Get Order Trades: Get order trades in the trading platform
- Get Margins: Get margins in the trading platform
- Get Holdings: Get holdings in the trading platform
- Get Positions: Get positions in the trading platform
- Get User Profile: Get user profile in the trading platform
- Get Stock News & Fundamentals: Gets news about a specific stock
Prerequisites
- Python
- Zerodha trading account with Personal API access from here
- Zerodha API key and secret
- Gemini API key or Application Default Credentials (for Google ADK Agent)
Installation
- Clone the repository:
git clone https://github.com/jainsourabh2/zerodha-mcp.git
cd zerodha-mcp
- Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
- Install dependencies:
pip3 install -r requirements.txt
- Set up environment variables:
# Copy the example environment file
cp .env.example .env
# Edit the .env file with your credentials
- Create a
.envfile with your configuration:
# Server Configuration
ZERODHA_API_KEY=your_api_key
ZERODHA_API_SECRET=your_api_secret
PORT=8001
SERVER_MODE=sse # or stdio
# Client Configuration
MCP_HOST=localhost
MCP_PORT=8001
GOOGLE_API_KEY=your_google_api_key
Server Usage
The server provides a set of tools for interacting with the Zerodha trading platform. To start the server:
-
Make sure your
.envfile is properly configured with your Zerodha API credentials. -
Start the server using one of the following methods:
# Using environment variables
python server.py
# Or using command line arguments
python server.py --api-key your_api_key --api-secret your_api_secret --port 8001 --mode sse
The server provides the following tools:
get_login_url: Get the login URL for user authenticationget_access_token: Generate access token using request tokenget_user_profile: Get user's Zerodha profile informationget_margins: Get available margins and fund detailsget_holdings: Get portfolio holdingsget_positions: Get current positionsget_orders: Get all orders for the dayget_order_history: Get history of a specific orderget_order_trades: Get trades generated by an orderplace_order: Place a new ordermodify_order: Modify an existing ordercancel_order: Cancel an order
Client Usage
This project provides three client implementations:
- Using Google ADK (
client/google_adk_client.py)
All clients connect to the MCP server and provide an interactive interface for trading operations.
### Running the Google ADK Client
1. Ensure you have authenticated with Google AI, either by setting the `GOOGLE_API_KEY` environment variable (and uncommenting it in `.env`) or by using Application Default Credentials (run `gcloud auth application-default login`).
2. Start the client using one of the following methods:
```bash
# Using environment variables from .env file
python client/google_adk_client.py
# Using command line arguments
python client/google_adk_client.py --host localhost --port 8001
# Using a combination (command line arguments take precedence)
MCP_HOST=localhost MCP_PORT=8001 python client/google_adk_client.py --host otherhost --port 9000
Client Configuration
Both clients support configuration through multiple sources, with the following precedence:
- Command-line arguments (highest precedence)
- Environment variables
.envfile variables
Configuration options:
- Environment variables:
MCP_HOSTandMCP_PORT - Command-line arguments:
--hostand--port .envfile variables:MCP_HOST,MCP_PORT,OPENAI_API_KEY, andGOOGLE_API_KEY
Default values (if no configuration is provided):
- Host: localhost
- Port: 8001
The client automatically loads environment variables from the .env file in the project root directory. Make sure your .env file contains the necessary configuration:
# Client Configuration
MCP_HOST=localhost
MCP_PORT=8001
OPENAI_API_KEY=your_openai_api_key
# GOOGLE_API_KEY=your_google_api_key
-
The client will automatically connect to the MCP server using the provided configuration.
-
Once connected, you can interact with the assistant using natural language commands. For example:
- "Show me my portfolio holdings"
- "What are my current positions?"
- "Place a market order for 10 shares of RELIANCE"
- "Cancel order ID 123456"
-
To exit the client, type 'quit' when prompted.
Development
Project Structure
client/google_adk_client.py: MCP client implementation using Google ADKserver.py: MCP server implementation with Zerodha API integrationgenerate_token.py: Utility for generating access tokensrequirements.txt: Project dependencies.env: Environment configuration
Contributing
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Acknowledgments
- Built using Google ADK
- Uses MCP for standardized communication
- Powered by KiteConnect for Zerodha API integration
zerodha-mcp
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.
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.
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
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.
E2B
Using MCP to run code via e2b.