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ambideXtrous9

StockMcp

by ambideXtrous9
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šŸ§  Stock Analysis using MCP Local LLM (Ollama Qwen3) with LangChain

This project demonstrates how to build a fully local AI assistant that provides detailed stock analysis using:

  • MCP (Model Context Protocol): Enables structured tool usage by the language model.

  • Ollama: A tool for running large language models locally.(Qwen3)

  • LangChain: A framework for developing applications powered by language models and run as MCP Client

  • BeautifulSoup: For web scraping financial data from Screener.in.

šŸ“¦ Features

šŸ” Company Details: Retrieve company name, current price, market cap, PE ratio, ROE, ROCE, and more.

šŸ“ˆ Profit Analysis: Extract quarterly and yearly net profit data.

šŸ‘„ Shareholding Patterns: Analyze holdings by promoters, DIIs, FIIs, and the public.

šŸ”§ Tool Integration: Seamless integration with MCP tools for enhanced functionality.

Related MCP server: sfinance-mcp-server

āš™ļø Configuration

MCP Server Setup

The MCP server is defined in mcp_config.json.

{
  "mcpServers": {
    "stock": {
      "command": "python",
      "args": ["StockMcp.py"],
      "transport": "stdio"
    }
  }
}


Give me the company details of CREDITACC.NS

šŸ› ļø Project Structure

ā”œā”€ā”€ StockMcp.py         # MCP server with tool definitions
ā”œā”€ā”€ requirements.txt    # Python dependencies
ā”œā”€ā”€ README.md           # Project documentation

šŸ“š Resources

MCP Github

Ollama Documentation

LangChain MCP Documentation

BeautifulSoup Documentation

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Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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