Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Yahoo Finance MCP Server get historical stock prices for AAPL over the last month"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that provides Yahoo Finance data through FastMCP v2 with HTTP streaming support. This server enables AI assistants to access real-time financial data, historical prices, company information, and market analysis tools. The server is primarily built for ThinkingSand AI agents, but can be deployed locally as well.
Real-time Stock Data: Current prices, historical data, and company information
Financial Statements: Income statements, balance sheets, and cash flow data
Options Trading: Option chains, expiration dates, and pricing data
Market News: Latest financial news and analysis
Institutional Data: Holder information and insider transactions
Analyst Coverage: Recommendations and upgrades/downgrades
HTTP Streaming: Fast, efficient data delivery with FastMCP v2
Related MCP server: Yahoo Finance MCP Server
Tool | Description | Parameters |
| Historical stock price data |
|
| Comprehensive company and stock information |
|
| Latest news articles for a stock |
|
| Dividend and stock split history |
|
| Financial statements (income, balance, cashflow) |
|
| Institutional and insider holder data |
|
| Available options expiration dates |
|
| Option chain data for calls and puts |
|
| Analyst recommendations and upgrades |
|
Python 3.11 or higher
uv package manager
# Clone the repository
git clone https://github.com/citizenhicks/yahoo-finance-mcp.git
cd yahoo-finance-mcp
# Install dependencies
uv sync
# Run the server
uv run python server.pyCreate a .env file (copy from .env.example):
# Server configuration
HOST=127.0.0.1 # Server host
PORT=8000 # Server port
LOG_LEVEL=INFO # Logging leveluv run python server.pyThe server will start on http://127.0.0.1:8000 by default and display:
Starting Yahoo Finance MCP Server locally on 127.0.0.1:8000...get_historical_stock_prices(ticker="AAPL", period="1mo", interval="1d")ticker: Stock symbol (e.g., "AAPL", "TSLA", "GOOGL")
period: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
interval: 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
get_stock_info(ticker="AAPL")Returns comprehensive company data including current price, market cap, financial metrics, and company details.
get_financial_statement(ticker="AAPL", financial_type="income_stmt")financial_type: income_stmt, quarterly_income_stmt, balance_sheet, quarterly_balance_sheet, cashflow, quarterly_cashflow
get_holder_info(ticker="AAPL", holder_type="institutional_holders")holder_type: major_holders, institutional_holders, mutualfund_holders, insider_transactions, insider_purchases, insider_roster_holders
# Get available expiration dates
get_option_expiration_dates(ticker="AAPL")
# Get option chain
get_option_chain(ticker="AAPL", expiration_date="2024-01-19", option_type="calls")option_type: calls or puts
get_recommendations(ticker="AAPL", recommendation_type="recommendations", months_back=12)recommendation_type: recommendations, upgrades_downgrades
months_back: Number of months for historical upgrades/downgrades
Add to your Claude Desktop MCP settings (~/.claude/claude_desktop_config.json):
{
"mcpServers": {
"yahoo-finance": {
"command": "uv",
"args": ["run", "python", "/path/to/yahoo-finance-mcp/server.py"],
"env": {
"HOST": "127.0.0.1",
"PORT": "8000"
}
}
}
}Connect directly to the HTTP endpoint:
http://127.0.0.1:8000import asyncio
from fastmcp import Client
async def example():
client = Client("http://127.0.0.1:8000")
async with client:
# Get Apple stock info
result = await client.call_tool("get_stock_info", {"ticker": "AAPL"})
print(result)
# Get historical prices
result = await client.call_tool("get_historical_stock_prices", {
"ticker": "AAPL",
"period": "1y",
"interval": "1d"
})
print(result)
asyncio.run(example())yahoo-finance-mcp/
├── server.py # Main FastMCP server implementation
├── main.py # Alternative entry point
├── pyproject.toml # Project dependencies and metadata
├── .env.example # Environment configuration template
├── .env # Your local environment variables (gitignored)
└── README.md # This documentationThe server includes robust error handling:
Invalid tickers: Returns clear error messages for non-existent symbols
Missing data: Graceful handling when specific data isn't available
Network issues: Proper error reporting for Yahoo Finance API issues
Parameter validation: Clear error messages for invalid parameters
Example error response:
"Error: Company ticker INVALID123 not found"All financial data is sourced from Yahoo Finance through the yfinance Python library:
Stock prices: Real-time and historical market data
Company information: Fundamentals, metrics, and company details
Financial statements: Official SEC filings and quarterly reports
Options data: Real-time options chains and pricing
News: Latest financial news and analyst coverage
Institutional data: Holdings and insider transaction data
FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install uv
RUN uv sync
EXPOSE 8000
CMD ["uv", "run", "python", "server.py"]HOST=0.0.0.0
PORT=8000
LOG_LEVEL=INFOFork the repository
Create a feature branch
Add new tools or improve existing ones
Update tests and documentation
Submit a pull request
This project is open source. Yahoo Finance data is subject to Yahoo's terms of service.
Server won't start: Check if port 8000 is available
Tool failures: Verify internet connection for Yahoo Finance API
Import errors: Ensure all dependencies are installed with uv sync
Set LOG_LEVEL=DEBUG in your .env file for detailed logging.
FastMCP Issues: FastMCP Documentation
Server Issues: Open an issue in this repository
Built with FastMCP v2 and yfinance
This server cannot be deployed
Your agent needs company financials it can compute on — statements, ratios, earnings, estimates, filings and insider activity as structured data, not a PDF. **What you can ask for** • "Give me 8 quarters of income statement, balance sheet and cash flow for this ticker." • "What do analysts estimate for next quarter, and how did the last four surprise?" • "Find this exact line item across every filing." • "Who bought or sold as an insider in the last 90 days?" • "Screen for profitable companies under this valuation with growing revenue." **How to use it** Point any MCP client at https://mcp.aisa.one/marketpulse/mcp and sign in with OAuth — there is no key to create or paste. 21 tools: prices and snapshots, income statements, balance sheets, cash-flow statements, financial metrics and snapshots, earnings, analyst estimates, company facts, filings and filing items, line-item search, a screener, insider trades, macro interest rates, news, plus EDINET documents and filing digests for Japanese issuers. **Why this rather than the source** Statements as fields you can compute on, and a screener in the same place. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Read the fundamentals here, then ask the same agent what social is saying about the ticker — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/finance/mcp for equities, crypto and prediction markets in one place.
Your agent needs markets — prices and fundamentals for listed companies, the filings behind them, crypto, and what the prediction markets put the odds at. **What you can ask for** • "Pull this company's income statement, cash flow and balance sheet for the last 8 quarters." • "What did insiders buy or sell, and when?" • "Snapshot prices for these 50 tickers, then the OHLC history for the three that moved." • "What are the current odds on this event across Kalshi and Polymarket?" • "Screen for companies matching these financial criteria." **How to use it** Point any MCP client at https://mcp.aisa.one/finance/mcp and sign in with OAuth — there is no key to create or paste. 49 tools: prices and snapshots, income statements, balance sheets and cash flows, metrics and ratios, earnings and analyst estimates, filings and line-item search, insider trades, macro interest rates, news, a screener; CoinGecko spot prices, market tables, OHLC, per-venue tickers and trending; Kalshi and Polymarket markets and trades; plus EDINET filings for Japan. **Why this rather than the source** Equities, crypto and event markets behind one account, so a cross-asset question is one conversation. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Read the number here, then ask the same agent what X is saying about the ticker today — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/marketpulse/mcp · /crypto-market-data/mcp · /prediction-market-data/mcp · /stock-pulse/mcp for one slice each.
Real SEC, 13F, insider, congress & macro data your AI agent can cite. Hosted MCP, 24 tools.
The Octagon MCP server provides specialized AI-powered financial research and analysis by integrating with the Octagon Market Intelligence API. It enables users to analyze public market data (SEC filings, earnings transcripts, financial metrics, and stock data for 8000+ companies), private market data (3M+ companies, 500k+ funding rounds, 2M+ M&A/IPO transactions), and conduct deep research including web scraping capabilities. The server also features autonomous research agents that search hundreds of sources and return fully cited reports in approximately one minute.
