SLoP5.0-NSEI

The NSEI Data Server is a FastMCP tool server designed to fetch daily trade data and market mover information from the National Stock Exchange (NSE) of India as part of the SLOP 2025 initiative. It exposes MCP endpoints that clients can query to retrieve up-to-date market data, historical daily trades, and market movers, enabling downstream analytics, dashboards, or automated trading workflows. Once running, you can connect your MCP client to the server name (nsei-data-server) and request data streams or individual data packets as defined by the FastMCP protocol. The server is intended to be lightweight and focused on NSEI data access, with a simple configuration that can be extended with API keys or additional data feeds if needed.

To use the server, start it with the configured command, then use your MCP client to request the available data types (e.g., daily trades, market movers). If you require authentication or rate limiting, configure the environment variables (such as NSE_API_KEY or LOG_LEVEL) or adjust server parameters as per your deployment needs. The project is designed to be extended with additional NSE data endpoints or data normalization layers to suit your analytics stack.

Prerequisites:

• Python 3.8+ (or a compatible Python runtime)
• Access to the repository containing the NSEI Data Server code (or a packaged module named nsei_data_server)
• Basic familiarity with FastMCP and MCP clients

Installation steps:

1. Ensure Python is installed:
   • On macOS/Linux: python3 --version
   • On Windows: py --version
2. Clone or download the repository containing the NSEI Data Server, or install the package if available:
   • git clone https://github.com/your-org/ossdaiict-slop5.0-nsei-mcp-server.git
   • cd ossdaiict-slop5.0-nsei-mcp-server
3. (Optional) Create a virtual environment:
   • python3 -m venv venv
   • source venv/bin/activate (Linux/macOS)
   • venv\Scripts\activate (Windows)
4. Install dependencies (adjust if a requirements.txt or pyproject.toml exists):
   • pip install -r requirements.txt
   • or: pip install -e . (if you are installing the package in editable mode)
5. Configure environment variables as needed (see notes below). Then run the server using the provided entry point:
   • python -m nsei_data_server

Notes:

• If the package name differs in your setup, adjust the -m module accordingly.
• Ensure any required NSE API keys or authentication details are provided via environment variables or config files.
• For deployment, consider containerization or systemd/supervisor to keep the server running.

Tips and considerations:

• Since the README currently contains placeholder sections (TODO), confirm the exact module name and entry point for your deployment (nsei_data_server in the example). Update the mcp_config accordingly.
• If you expose API keys or secrets, store them securely (e.g., environment variables, secret management service) and avoid hard-coding in code or config.
• Validate MCP endpoints with a local client before deploying to production. Keep an eye on rate limits and data freshness requirements for NSE data.
• Include error handling and observability (logging, metrics) to assist in troubleshooting in production environments.
• If you switch to a different runtime (e.g., Node.js), update the mcp_config command/args accordingly and provide any necessary env vars.