MCP Connector

Access Real-Time Stock Data and Analysis

MCP YFinance Stock Server gives agents real-time stock prices, watchlist management, and full technical/volatility analysis tools.

Works with yfinance

90
Spark score
out of 100
Status Verified
Updated 2 months ago
Version 1.0.0
Models
universal

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Why it matters

Integrate with Yahoo Finance to provide AI agents with real-time stock prices, watchlist management, and comprehensive technical analysis tools.

Outcomes

What it gets done

01

Retrieve current stock prices for any ticker.

02

Manage and retrieve prices for a custom stock watchlist.

03

Perform technical analysis including RSI, MACD, and MA.

04

Analyze stock trends and calculate volatility metrics.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-yfinance | bash

Capabilities

Tools your agent gets

add_to_watchlist

Add a stock ticker to your personal watchlist

analyze_stock

Perform monthly technical analysis of trends (RSI, MACD, MA)

get_technical_summary

Create a comprehensive technical summary with indicators and signals

get_watchlist_prices

Get current prices for all tickers in your watchlist

get_trend_analysis

Analyze recent trend changes, patterns, and divergences

get_stock_price

Get the current price for a specified ticker symbol

get_volatility_analysis

Calculate historical volatility and ATR metrics

compare_stocks

Compare prices of two stocks for relative performance analysis

Overview

yfinance MCP Server

MCP YFinance Stock Server exposes stock price, watchlist, and technical analysis tools to an AI agent, built on the Yahoo Finance API. It covers price lookups, comparisons, watchlist management, RSI/MACD trend analysis, and volatility/ATR risk metrics among its 18 tools. Use it when an agent needs live stock data plus derived technical or volatility analysis, or when Claude Desktop should chart the results directly. Prices are described by the project as real-time(ish), not guaranteed sub-second.

What it does

MCP YFinance Stock Server is a stock price server built on the Yahoo Finance (YFinance) API for MCP clients. It lets an AI agent retrieve real-time stock data, manage a personal watchlist, run full stock and technical analysis, and pull technical indicators, exposed through tools including get_stock_price (current price for a ticker), compare_stocks (compare two stocks for relative performance), add_to_watchlist and get_watchlist_prices (manage and fetch prices for a watchlist), analyze_stock (a one-month technical trend analysis covering RSI, MACD, and moving averages), get_technical_summary (a comprehensive summary combining indicators and signals), get_trend_analysiss (recent trend shifts, patterns, and divergences), and get_volatility_analysis (historical volatility and ATR metrics) - the project's own tool reference lists these among 18 tools total, with the README's example queries also naming a get_stock_history tool for pulling historical price data.

When to use - and when NOT to

Use it when an agent needs live or recent stock data plus derived analysis - technical indicators, trend detection, volatility or risk metrics, or side-by-side comparisons - rather than raw price lookups alone, or when you want Claude Desktop to fetch data and render it as a chart directly in the conversation. It's well suited to building a stock-tracking bot or financial dashboard incrementally, since the watchlist tools let you build up a tracked set of tickers over multiple calls. Skip it if you need guaranteed real-time, sub-second market data - the project itself describes prices as "real-time(ish)" - or if you'd rather start with something simpler first: the author explicitly recommends trying a companion crypto price tracker project before this fuller stock server, to learn the MCP plus FastAPI pattern on a smaller surface first.

Capabilities

  • Real-time(ish) stock price lookups and multi-stock comparisons via get_stock_price and compare_stocks.
  • Watchlist management: add tickers with add_to_watchlist and fetch current prices for the whole list with get_watchlist_prices.
  • Technical analysis: analyze_stock runs a one-month trend analysis (RSI, MACD, moving averages), get_technical_summary produces a combined indicators-and-signals summary, and get_trend_analysiss surfaces trend shifts and divergences.
  • Risk assessment via get_volatility_analysis, calculating historical volatility and ATR metrics.
  • An interactive MCP Server Inspector (mcp dev source/yf_server.py) to browse available tools and resources, test input/output per tool, and monitor real-time responses during development.
  • Claude Desktop can fetch data through the server and render it as a chart directly in the conversation, per the project's example queries.

How to install

Set up the environment with uv:

# 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

Then, once pyproject.toml has its dependencies, install the project (uv pip install -e .) and start the server with uv run main.py. To wire it into an MCP client, add an entry to mcp.config.json:

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

Replace the placeholder path with the actual project directory, then restart Claude Desktop (or any MCP-based client) to load the new tools.

Who it's for

Developers and traders building a stock-tracking bot or financial dashboard who want technical analysis, trend detection, and volatility metrics available to an AI agent alongside basic price and watchlist tools, especially anyone already comfortable with the MCP and uv workflow from a smaller companion project. Licensed under MIT.

Source README

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πŸ’Ή MCP YFinance Stock Server

Python
MCP
License
Listed on Spark
Install via Spark

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

image


πŸͺ™ 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 πŸ‘‡

πŸ”— 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

πŸ”— Related Projects

Explore more of my work:

  • GenAI-Learnings: A deep dive into Generative AI concepts, tools, and projects.
  • LangChain-Mastery: Everything you need to master LangChain for building powerful LLM applications.
  • Complete Data Science: A deep dive into Data Science AI concepts, tools, and projects and all the material for learning and interview preparation.
  • Reinforcement-Learning: Hands-on experiments and theory in Reinforcement Learning.
  • CompleteRAG: End-to-end implementation of Retrieval-Augmented Generation (RAG) systems.

πŸ“ˆ Then Level Up: Build the yFinance Stock Server

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

πŸ”— GitHub Repo:
https://github.com/Adity-star/mcp-yfinance-server

πŸ“ Detailed Blog:
πŸ‘‰ 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

πŸ“¦ Step 1: Set Up the Environment (with uv)

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

πŸ› οΈ 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

πŸ“₯ Install the project

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

#  Run 
uv pip install -e .

πŸš€ Step 2: Running the MCP Server

Once your environment is ready, start the stock server:

uv run main.py

πŸ§ͺ Want a quick test first?
Try checking the tools first:

python run test_server_fun.py

πŸ“„ Curious how the full server works?

Explore the source code here:

πŸ”— yf_server.py β€Ί GitHub


πŸ› οΈ 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:

πŸ“¦ 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).

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

🧠 Use Cases

These tools are ideal for:

  • πŸ“Š Dynamic watchlist management
  • πŸ” Trend and momentum detection
  • πŸ“ˆ Deep-dive technical analysis for investment decisions
  • ⚠️ Volatility-based risk assessment
  • πŸ€– Powering stock-focused autonomous agents or dashboards

βš™οΈ Keep this reference handy for building intelligent financial applications with the MCP server.


πŸ” 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
  • πŸ“₯ Test input/output for each tool
  • πŸ“‘ Monitor real-time responses from your server

image


βš™οΈ 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.
πŸ’‘ Tip: Rename your server from crypto-price-tracker to yfinance-price-tracker for clarity.


πŸ” 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.


βœ… 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."
β†’ πŸ”§ Uses compare_stocks

"Get the historical data for Tesla over the past month."
β†’ πŸ“Š Uses get_stock_history

"Add Apple, Tesla, and Reliance to my watchlist."
β†’ πŸ“‹ Uses add_to_watchlist

"Show me a chart of Apple’s stock over the last 30 days."
β†’ πŸ–ΌοΈ Claude can fetch + visualize data using your server

πŸ“· Sample Chart:
πŸ–Ό view Screenshot

🌐 Live Claude Site:
Open Demo on Claude.site

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


πŸ“Š Results

βš™οΈ Outcomes You Can Expect

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

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


πŸ“« Feedback & Contributions

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

πŸ› Reporting Issues

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

  • βœ… Steps to reproduce (if applicable)
  • πŸ” Expected vs. actual behavior
  • πŸ“· Screenshots or error logs (if relevant)

πŸ“¬ 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!


πŸ’¬ 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
  • πŸ“² Tag me on X @AdityaAkuskar - I’d love to see what you build!
  • πŸ”— Connect with me on LinkedIn

πŸ“œ License

MIT Β© 2025 Ak Aditya.


πŸš€ Let’s build better tools together.

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

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