mcp-stock-analysis 0.1.1

MCP server for stock portfolio analysis and reporting

📊 Stock Analysis MCP Server

This is a modular, async-capable Multi-Component Protocol (MCP) server for comprehensive stock portfolio analysis using yfinance, pandas, numpy, and BeautifulSoup. It exposes a set of tools for use by LLM agents or other MCP clients via the uvx ecosystem.

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🚀 Features

• Fetch historical market data and fundamentals
• Analyze volatility, RSI, moving averages, drawdowns, P&L
• Sentiment scraping via Yahoo Finance
• Portfolio aggregation (returns, weight concentration, volatility)
• Async parallel processing for multiple tickers
• Generates markdown-based report summaries with insights

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🧠 UVX Integration Config

Include this block in your UVX configuration:

{
  "name": "mcp_stock_analysis",
  "command": "uvx",
  "args": [
    "mcp-stock-analysis@latest"  /// The package name and executable name are different
  ]
}

This MCP module will then be discoverable and callable by your agents or chains.

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🧰 Tools Overview

fetch_market_data(symbol)

Fetches OHLCV history, company info, dividends, splits, analyst recommendations, and insider transactions for a stock.

fetch_market_data_multiple_tickers_parallel(symbols)

Parallel async variant of above for multiple stocks.

analyze_stock(data, qty, avg_cost)

Computes:

• Invested amount, current value, P&L
• Volatility (annualized), drawdown
• RSI, MA50, MA200

analyze_multiple_stocks_parallel(stock_inputs)

Parallelized multi-stock analyzer. Accepts a list of dictionaries with each stock’s market data, quantity, and average cost.

summarize_sentiment(symbol)

Scrapes Yahoo Finance news headlines for the stock. Returns recent 5 with title + timestamp.

aggregate_portfolio(analyses)

Computes overall portfolio metrics:

• Total invested, current value, return %
• Concentration risks (stocks >10%)
• Weighted volatility

generate_recommendations(summary, analyses)

Alerts for:

• Overweight positions (>20%)
• High RSI (>70)
• Concentration risks

render_report(analyses, summary, recommendations)

Outputs full Markdown report with tables, highlights, and headlines.

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🧪 Example Workflow (in code)

symbols = ["AAPL", "TSLA", "INFY.NS"]
data_list = await fetch_market_data_multiple_tickers_parallel(symbols)

# Example: user holds shares of each
inputs = [
    {"data": data_list[0], "qty": 10, "avg_cost": 120},
    {"data": data_list[1], "qty": 5, "avg_cost": 700},
    {"data": data_list[2], "qty": 15, "avg_cost": 1500}
]

analyses = await analyze_multiple_stocks_parallel(inputs)
summary = await aggregate_portfolio(analyses)
recommendations = await generate_recommendations(summary, analyses)
report = await render_report(analyses, summary, recommendations)

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📎 Notes

• Designed to be called as an MCP server over stdio using uvx run
• Ensure Python packages yfinance, pandas, beautifulsoup4, requests, numpy are installed in the environment
• MCP tools are fully async and support concurrent processing for scalability

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📤 Author / Contact

This module was built as part of an intelligent portfolio assistant. For integration help or feedback, reach out via the associated MCP registry or support forums.

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📌 License

MIT License (or customize as needed)

To Re-Build

Update .toml file with a version bump

'uv sync' will update every packages 'uv build' will build the package 'uv publish --token <your_token>' will publish the package to the MCP registry