NSE Ticker MCP Server

A Model Context Protocol (MCP) server that provides access to National Stock Exchange (NSE) data through Upstox API.

Tools

The server exposes the following tools through the Model Context Protocol:

Historical Data

Related MCP server: Angel One MCP Server

Features

• Real-time NSE stock data access

• Multiple time intervals support (minute, 30minute, day, week, month)

• Error handling and data validation

• Historical price data with customizable date ranges

Requirements

• Python 3.11 or higher

• Dependencies (specified in pyproject.toml):

  • mcp

  • pandas

  • httpx

  • pydantic

Setup

1. Clone this repository:

git clone https://github.com/sougatabh/nse-ticker-mcp.git
cd nse-ticker-mcp

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -e .

Usage

Development Mode

Test the server with MCP Inspector:

python ticker.py

Integration with Claude for Desktop

1. Install Claude for Desktop

2. Edit Claude's config file:

MacOS:

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

Add the following configuration:

{
  "mcpServers": {
    "nse-ticker": {
      "command": "python",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/nse-ticker-mcp",
        "ticker.py"
      ]
    }
  }
}

Example Usage

# Get historical data for HDFC Bank
response = await get_historical_candle(
    symbol="HDFCBANK",
    from_date="2024-01-01",
    to_date="2024-03-31",
    interval="day"
)

Available Intervals

• minute - 1-minute candles

• 30minute - 30-minute candles

• day - Daily candles

• week - Weekly candles

• month - Monthly candles

Error Handling

The server provides detailed error messages for:

• Invalid symbols

• Connection issues

• API errors

• Data validation failures

• Date format issues

Configuration

Stock symbols and instrument keys are configured in:

• configs/NSE.csv

License

MIT License

Contributing

1. Fork the repository

2. Create your feature branch

3. Submit a pull request