StockScreener-MCP-with-Ollama-and-Langchain
このプロジェクトは、MCPを利用してローカルで動作するAIアシスタントを構築し、詳細な株式分析を提供します。OllamaとLangChainを活用し、財務データのウェブスクレイピングを行うことで、企業情報や利益分析、株主構成を分析する機能を備えています。
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README
🧠 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.
📦 Features
🔍 Company Details: Retrieve company name, current price, market cap, PE ratio, ROE, ROCE, and more.
📈 Profit Analysis: Extract quarterly and yearly net profit data.
👥 Shareholding Patterns: Analyze holdings by promoters, DIIs, FIIs, and the public.
🔧 Tool Integration: Seamless integration with MCP tools for enhanced functionality.
⚙️ 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
🛠️ Project Structure
├── StockMcp.py # MCP server with tool definitions
├── requirements.txt # Python dependencies
├── README.md # Project documentation
📚 Resources
MCP Github
Ollama Documentation
LangChain MCP Documentation
BeautifulSoup Documentation