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Airbnb-StockScreener-Agent-with-MCP

by ambideXtrous9·★ 0·Score 27

A local stock analysis MCP server using LangChain, Ollama Qwen3, and BeautifulSoup for financial data.

financeai-llmweb-scraping
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Overview

This project creates a fully local AI assistant that performs detailed stock analysis using the MCP protocol. It uses Ollama with Qwen3 as the local language model, LangChain as the framework for MCP client implementation, and BeautifulSoup for web scraping financial data from Screener.in. The server provides tools to retrieve company details, profit analysis data, and shareholding patterns. The implementation demonstrates how to integrate MCP with local LLMs for financial analysis applications.

Try asking AI

After installing, here are 5 things you can ask your AI assistant:

you:Perform detailed stock analysis for individual companies
you:Extract financial metrics and profit trends for investment research
you:Analyze shareholding patterns and institutional ownership
you:What financial data sources does this MCP server use?
you:Can I use other local language models besides Qwen3?

When to choose this

Choose this for local stock analysis without requiring external API calls, particularly when working with Indian stocks.

When NOT to choose this

Avoid if you need real-time stock data or analysis of international markets beyond what Screener.in covers.

Tools this server exposes

3 tools extracted from the README
  • get_company_details

    Retrieve company name, current price, market cap, PE ratio, ROE, ROCE, and more

  • get_profit_analysis

    Extract quarterly and yearly net profit data

  • get_shareholding_pattern

    Analyze holdings by promoters, DIIs, FIIs, and the public

Note: Tool names inferred from feature descriptions in the README. The exact tool signatures are not documented.

Comparable tools

yfinance-mcpalpha-vantage-mcppolygon-io-mcp

Installation

  1. Install prerequisites:

``bash pip install langchain langchain-community langchain-core beautifulsoup4 ollama ollama pull qwen3 ``

  1. Clone the repository and install requirements:

``bash git clone <repository_url> pip install -r requirements.txt ``

  1. Configure MCP server in Claude Desktop (or compatible client):

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

FAQ

What financial data sources does this MCP server use?
The server primarily uses Screener.in as the data source, with BeautifulSoup for web scraping to extract financial information.
Can I use other local language models besides Qwen3?
The implementation is designed to work with Ollama, which supports multiple models. You can modify the code to use different models available through Ollama.

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Last updated · Auto-generated from public README + GitHub signals.