yfinance

What is mcp-yfinance-server?

mcp-yfinance-server is a stock price server powered by the Yahoo Finance (YFinance) API, built for seamless integration with MCP (Multi-Agent Control Protocol). It allows AI agents or clients to retrieve real-time stock data, manage watchlists, perform full stock analysis, and run technical indicators.

How to use mcp-yfinance-server?

Set Up the Environment: Use uv (a fast Python package manager) to set up your project environment. Install uv, create a project directory, initialize it, and activate the virtual environment.
Install the Project: Once your pyproject.toml is set up with dependencies, run uv install .
Run the MCP Server: Copy yf_serve.py to your directory and run uv run source/yf_server.py. A lightweight demo server is also available.
Inspect the MCP Server: Use mcp dev source/yf_server.py to launch an interactive UI to view tools, test input/output, and monitor responses.
Configure Your MCP Server: Add the server entry to your mcp.config.json file, replacing absolute paths with your actual file paths.
Restart Claude Desktop: Restart Claude Desktop (or any MCP-using interface) to activate the new YFinance tools.
Test the MCP Server: Use example queries in Claude Desktop to test the integration, such as comparing stock prices, getting historical data, or adding to a watchlist.

Key Features of mcp-yfinance-server

Real-time Stock Data: Retrieve current stock prices and historical data.
Watchlist Management: Add and manage stock tickers in a personal watchlist.
Comprehensive Stock Analysis: Perform 1-month technical trend analysis (RSI, MACD, MAs).
Technical Summaries: Generate comprehensive technical summaries including indicators and signals.
Trend Analysis: Analyze recent trend shifts, patterns, and divergences.
Volatility Analysis: Calculate historical volatility and ATR metrics.
Stock Comparison: Compare two stock prices for relative performance analysis.
Integration with MCP: Exposes 18 powerful tools for AI agents and CLI users.
Dashboard and Visualizations: Ready to power AI agents or dashboards to visualize stock data, including technical charts and visual insights via Claude Desktop.

Use Cases of mcp-yfinance-server

Dynamic watchlist management
Trend and momentum detection
Deep-dive technical analysis for investment decisions
Volatility-based risk assessment
Powering stock-focused autonomous agents or dashboards

FAQ from mcp-yfinance-server

How to report issues? Open an issue in the GitHub Issues section, including steps to reproduce, expected vs. actual behavior, and screenshots/error logs if relevant.
How to contribute? Submit a Pull Request in the GitHub Pull Requests section.
Where can I find the complete tools list? The complete tools list is available here.
Is there a simpler project to start with? Yes, the developer recommends starting with the mcp-crypto-server for learning how to use MCP and build APIs with FastAPI before moving to this project.

💹 MCP YFinance Stock Server

This project sets up a stock Price server powered by the Yahoo Finance (YFinance) API and built for seamless integration with MCP (Multi-Agent Control Protocol).

It allows AI agents or clients to:

Retrieve real-time stock data
Manage a watchlist
Perform full stock analysis
Run full technical indicators
And much more

🪙 Start Simple: Build a Crypto Price Tracker First

Before diving into the full-blown stock server, I recommend starting with this simple crypto tracker built with Python + MCP 👇

🔗 GitHub Repo: https://github.com/Adity-star/mcp-crypto-server

You'll learn how to:

Use MCP to expose crypto tools like get_price("BTC")

Build an API with FastAPI

Fetch real-time prices using the Alpaca API

📈 Then Level Up: Build the yFinance Stock Server

Once you're familiar with the flow, move on to this more advanced stock tracker 💹

🔗 GitHub Repo: https://github.com/Adity-star/mcp-yfinance-server

📝 Detailed Blog: 👉 How I Built My Own Stock Server with Python, yFinance, and a Touch of Nerdy Ambition

Includes:

Watchlists

Real-time(ish) price updates

Technical summaries

A full-featured dashboard

Trend + momentum indicators

Watchlist management

📦 Step 1: Set Up the Environment (with uv)

We use uv — a modern, ultra-fast Python package manager — to manage our project environment.

🛠️ Installation & Setup

Run the following commands in your terminal:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh 

# Create and navigate to your project directory
mkdir mcp-yfinance-server
cd mcp-yfinance-server

# Initialize a new project
uv init

# Create and activate the virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

📥 Install the project

Once your pyproject.toml is set up with dependencies, run:

#  Run 
uv install .

🚀 Step 2: Running the MCP Server

Once your environment is ready, start the stock server:

cp ../yf_serve.py .
uv run source/yf_server.py

🧪 Want a quick test first? Try running the lightweight demo server:

uv run demo_stock_price_server.py

📄 Curious how the full server works?

Explore the source code here:

🔗 yf_server.py › GitHub

🛠️ MCP Tool Reference

The server exposes many tools for AI agents and CLI users.
Here are some important tools, check out the complete tools list here:

📦 Tool List

Tool Name	Description
`add_to_watchlist`	Add a stock ticker to your personal watchlist.
`analyze_stock`	Perform a 1-month technical trend analysis (RSI, MACD, MAs)..
`get_technical_summary`	Generate a comprehensive technical summary including indicators & signals..
`get_watchlist_prices`	Fetch the most recent prices for all watchlisted tickers.
`get_trend_analysiss`	Analyze recent trend shifts, patterns, and divergences..
`get_stock_price`	Retrieve the current price for a given ticker symbol.
`get_volatility_analysis`	Calculate historical volatility and ATR metrics..
`compare_stocks`	Compare two stock prices (useful for relative performance analysis).

✅ Total: 18 powerful tools to analyze and monitor stocks with precision.

🧠 Use Cases

These tools are ideal for:

📊 Dynamic watchlist management
🔁 Trend and momentum detection
📈 Deep-dive technical analysis for investment decisions
⚠️ Volatility-based risk assessment
🤖 Powering stock-focused autonomous agents or dashboards

⚙️ Keep this reference handy for building intelligent financial applications with the MCP server.

🔍 Step 3: Inspecting the MCP Server

Easily explore and test your MCP tools using the MCP Server Inspector. Run the following command in your terminal:

$ mcp dev source/yf_server.py

This launches an interactive UI to:

🧰 View all available tools and resources
📥 Test input/output for each tool
📡 Monitor real-time responses from your server

⚙️ Step 4: Configure Your MCP Server

To integrate your YFinance MCP server, add the following entry to your mcp.config.json file:

{
  "mcpServers": {
    "yfinance-price-tracker": {
      "command": "/ABSOLUTE/PATH/TO/uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/YOUR/mcp-yfinance-server",
        "run",
        "yf_server.py"
      ]
    }
  }
}

⚠️ Replace /ABSOLUTE/PATH/TO/... with actual file paths. 💡 Tip: Rename your server from crypto-price-tracker to yfinance-price-tracker for clarity.

🔁 Step 5: Restart Claude Desktop

Restart Claude Desktop (or any interface that uses MCP) to reload and activate your new YFinance tools.

This ensures the updated MCP configuration is recognized and all stock tracking tools are ready to use.

✅ Step 6: Testing the MCP Server with Claude Desktop

With everything installed and configured, you're ready to test your MCP server in Claude Desktop.

Use these example queries to test your MCP YFinance Server in action:

"Compare the stock prices of Tesla and Apple." → 🔧 Uses compare_stocks

"Get the historical data for Tesla over the past month." → 📊 Uses get_stock_history

"Add Apple, Tesla, and Reliance to my watchlist." → 📋 Uses add_to_watchlist

"Show me a chart of Apple’s stock over the last 30 days." → 🖼️ Claude can fetch + visualize data using your server

📷 Sample Chart: 🖼 view Screenshot

🌐 Live Claude Site: Open Demo on Claude.site

🧪 These tests ensure your MCP integration is working end-to-end—from data retrieval to real-time analysis and visualization.

📊 Results

⚙️ Outcomes You Can Expect

Feature	Outcome
✅ Stock Analysis	Analyse stock giving price, OHLC, returns, volume, insights and data.
📈 Technical Analysis	Access indicators like RSI, MACD, MA, and a complete technical summary.
📉 Volatility Reports	Analyze stock risk with ATR and volatility metrics.
🔍 Trend Analysis	Detect trend shifts and divergence using price movement analysis.
🧠 Visualisations	18+ tools ready to power AI agents or dashboards to visualise stock.
📋 Technical Charts	Analyse and monitor technical indicators for stocks in real-time.
🖼️ Visual Insights	Generate charts and visual summaries with Claude Desktop.

🎉 Ready to build your stock-tracking bot or intelligent financial dashboard? This project has all the core pieces.

📫 Feedback & Contributions

Contributions are welcome! Whether you're fixing bugs, adding features, or improving documentation, your help makes this project better.

🐛 Reporting Issues

If you encounter bugs or have suggestions, please open an issue in the Issues section. Be sure to include:

✅ Steps to reproduce (if applicable)
🔍 Expected vs. actual behavior
📷 Screenshots or error logs (if relevant)

📬 Submit a Pull Request

Have a fix or improvement? Head over to the Pull Requests section and submit your PR. We’ll review and merge it ASAP!

💬 Spread the Word

If this project saved you from API rate limits or overpriced SaaS tools...

🌟 Star the repo
🍴 Fork it and build your own crypto/stock tool
📲 Tag me on X @AdityaAkuskar — I’d love to see what you build!
🔗 Connect with me on LinkedIn

📜 License

🚀 Let’s build better tools together.

If you’d like a tweet thread, carousel, or launch post for this — I’ve got your back 😎

yfinance Overview