Mcp Yfinance Stock Server

What is Mcp Yfinance Stock Server about?

Mcp Yfinance Stock Server enables AI agents, CLI tools, or custom applications to query live stock prices, maintain personal watchlists, and run technical analyses such as RSI, MACD, moving averages, volatility and trend detection. All functionality is exposed as MCP tools that can be called programmatically.

How to use Mcp Yfinance Stock Server?

1. Set up the environment – install the ultra‑fast Python package manager uv, initialise a project, create and activate a virtual environment, then install the package in editable mode:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   mkdir mcp-yfinance-server && cd mcp-yfinance-server
   uv init
   uv venv
   source .venv/bin/activate   # Windows: .venv\Scripts\activate
   uv pip install -e .
   

2. Run the server:

   uv run main.py
   

3. Inspect tools (optional) with the MCP inspector:

   mcp dev source/yf_server.py
   

4. Integrate by adding an entry to your mcp.config.json (replace the absolute path with your location):

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

5. Restart any MCP‑aware client (e.g., Claude Desktop) to load the new tools.

Key features of Mcp Yfinance Stock Server

• Real‑time price retrieval (get_stock_price)
• Watchlist management (add_to_watchlist, get_watchlist_prices)
• Technical analysis (analyze_stock, get_technical_summary, get_trend_analysiss)
• Volatility & risk metrics (get_volatility_analysis)
• Stock comparison (compare_stocks)
• 18+ ready‑to‑use MCP tools covering history, indicators, and visualisations
• FastAPI based HTTP endpoint for easy integration with web or AI agents

Use cases of Mcp Yfinance Stock Server

• Building autonomous trading bots that request technical signals on demand
• Powering financial dashboards that display live charts and indicator overlays
• Enabling AI assistants (e.g., Claude Desktop) to answer stock‑related queries instantly
• Creating custom CLI utilities for portfolio monitoring and risk assessment
• Supplying data to research notebooks or back‑testing frameworks via simple HTTP calls

FAQ from the Mcp Yfinance Stock Server

Q: Do I need an API key for YFinance? A: No. YFinance scrapes publicly available Yahoo Finance data and works without authentication.

Q: Can I run the server on Windows? A: Yes. Use the provided uv commands; activate the virtual environment with .venv\Scripts\activate.

Q: How many stocks can I add to the watchlist? A: The watchlist is stored in memory; it can handle dozens of tickers comfortably. For larger sets, persist the list externally.

Q: Is there a Docker image available? A: The repository does not include a Dockerfile, but you can containerise it by installing uv inside a Python base image and running uv run main.py.

Q: How do I test the tools without a full client? A: Run python run test_server_fun.py to execute a quick sanity check of the exposed MCP functions.

💹 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 (Model 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

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🪙 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

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📈 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

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📦 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 pip install -e .

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🚀 Step 2: Running the MCP Server

Once your environment is ready, start the stock server:

uv run main.py

🧪 Want a quick test first? Try checking the tools first:

python run test_server_fun.py

📄 Curious how the full server works?

Explore the source code here:

🔗 yf_server.py › GitHub

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🛠️ 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.

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🔍 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

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⚙️ 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": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/YOUR/mcp-yfinance-server",
        "run",
        "main.py"
      ]
    }
  }
}

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

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🔁 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.

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✅ 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.

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📊 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.

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📫 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!

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💬 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

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📜 License

MIT © 2025 Ak Aditya.

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🚀 Let’s build better tools together.

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