Zentickr Yahoo Query Mcp Server
chintan-diwakar/zentickr-yahoo-query-mcp · 14 stars · Python · MIT
MCP server A comprehensive MCP server providing financial data through unofficial Yahoo Finance APIs for educational purposes. Features 20+ tools including real-time stock prices, financial statements, analyst recommendations, ESG scores, company profiles, and historical data. Built with Py
Install
The repo has no one-line install. Follow its README.
Files
Zentickr — Yahoo Finance MCP Server
Zentickr is a stdio-based Model Context Protocol (MCP) server that gives AI assistants access to 23 Yahoo Finance tools through the yahooquery library—covering prices, fundamentals, statements, analyst research, ownership, events, and symbol discovery.
⚠️ Important Disclaimer
This software is provided for educational and informational purposes only. It does not provide financial, investment, legal, or tax advice. Market data may be delayed, incomplete, or inaccurate. You are solely responsible for your investment decisions; the authors are not liable for any losses or damages arising from use of this software.
Quickstart
git clone https://github.com/chintan-diwakar/zentickr-yahoo-query-mcp.git cd zentickr-yahoo-query-mcp python -m venv .venv && source .venv/bin/activate pip install -e .
Run the server over stdio:
zentickr
Or:
python -m zentickr
Client setup
Claude Desktop
Add this to claude_desktop_config.json:
{
"mcpServers": {
"zentickr": {
"command": "/absolute/path/to/zentickr-yahoo-query-mcp/.venv/bin/zentickr"
}
}
}
Claude Code
claude mcp add zentickr -- /absolute/path/to/zentickr-yahoo-query-mcp/.venv/bin/zentickr
Cursor
Create or update .cursor/mcp.json:
{
"mcpServers": {
"zentickr": {
"command": "/absolute/path/to/zentickr-yahoo-query-mcp/.venv/bin/zentickr"
}
}
}
Real-world use cases
Compare Apple and Microsoft fundamentals
Prompt: “Compare Apple and Microsoft fundamentals.”
The assistant can call get_financial_data(symbols="AAPL,MSFT") and get_valuation_measures(symbols="AAPL,MSFT").
Captured output excerpt:
Financial Data:
{
"AAPL": {
"currentPrice": 333.26,
"targetHighPrice": 400.0,
"targetLowPrice": 215.0,
"targetMeanPrice": 315.78604,
"recommendationKey": "buy",
"numberOfAnalystOpinions": 43,
"totalCash": 68507000832,
"totalDebt": 84710998016,
"totalRevenue": 451442016256,
"earningsGrowth": 0.218,
"revenueGrowth": 0.166
}
}
Valuation Measures:
[
{
"symbol": "AAPL",
"asOfDate": "2026-05-28 00:00:00",
"periodType": "TTM",
"ForwardPeRatio": 35.3357,
"MarketCap": 4589945623560.0,
"PbRatio": 43.101723,
"PeRatio": 37.83414,
"PegRatio": 2.7194
}
]
Prep me for AAPL earnings
Prompt: “Prep me for AAPL earnings.”
The assistant can call get_calendar_events(symbols="AAPL") and get_earnings_trend(symbols="AAPL").
Captured output excerpts:
Calendar Events:
{
"AAPL": {
"earnings": {
"earningsDate": [
"2026-07-31 01:30:S"
],
"earningsAverage": 1.89396,
"earningsLow": 1.83,
"earningsHigh": 1.99,
"revenueAverage": 108890141050
}
}
}
Earnings Trend:
{
"AAPL": {
"trend": [
{
"period": "0q",
"endDate": "2026-06-30",
"growth": 0.20629999,
"earningsEstimate": {
"avg": 1.89396,
"numberOfAnalysts": 31,
"yearAgoEps": 1.57
}
}
]
}
}
Find NVIDIA's ticker and current price
Prompt: “Find NVIDIA's ticker and current price.”
The assistant can call search_symbols(query="nvidia"), then get_price_data(symbols="NVDA").
Captured output excerpts:
Search Results for 'nvidia':
[
{
"symbol": "NVDA",
"name": "NVIDIA Corporation",
"type": "EQUITY",
"exchange": "NMS"
}
]
Price Data:
{
"NVDA": {
"preMarketPrice": 202.155,
"regularMarketPrice": 207.4,
"regularMarketDayHigh": 211.08,
"regularMarketDayLow": 205.85,
"regularMarketVolume": 116253420,
"regularMarketPreviousClose": 212.5,
"exchangeName": "NasdaqGS",
"marketState": "PRE",
"symbol": "NVDA"
}
}
Tool catalog
Market Data
| Tool | Arguments | Returns |
|---|---|---|
get_price_data |
symbols: str |
Current price and trading data |
get_summary_detail |
symbols: str |
Day range, volume, dividend, and 52-week range |
get_historical_prices |
symbols: str, period: str | None = None, start_date: str | None = None, end_date: str | None = None, interval: str = "1d" |
Historical OHLCV price data |
get_calendar_events |
symbols: str |
Upcoming earnings dates and dividend events |
Financial Statements
| Tool | Arguments | Returns |
|---|---|---|
get_financial_data |
symbols: str |
Key financial data, including targets, margins, cash, debt, and growth |
get_balance_sheet |
symbols: str, frequency: str = "annual" |
Balance sheet data |
get_cash_flow |
symbols: str, frequency: str = "annual" |
Cash flow statement |
get_income_statement |
symbols: str, frequency: str = "annual" |
Income statement |
get_valuation_measures |
symbols: str |
Market cap, P/E, EV, and price/book measures |
get_earnings |
symbols: str |
Earnings data |
get_earnings_trend |
symbols: str |
Earnings trend estimates |
Analysis & Research
| Tool | Arguments | Returns |
|---|---|---|
get_recommendations |
symbols: str |
Analyst recommendations |
get_recommendation_trend |
symbols: str |
Strong buy, buy, hold, sell, and strong sell counts |
get_technical_insights |
symbols: str |
Technical analysis insights |
get_esg_scores |
symbols: str |
Environmental, social, and governance scores |
get_company_profile |
symbols: str |
Company sector, industry, description, and address |
Ownership & Governance
| Tool | Arguments | Returns |
|---|---|---|
get_major_holders |
symbols: str |
Major holders breakdown |
get_institution_ownership |
symbols: str |
Institutional ownership |
get_insider_holders |
symbols: str |
Insider holders |
get_insider_transactions |
symbols: str |
Insider transactions |
get_fund_ownership |
symbols: str |
Mutual fund and ETF ownership |
get_company_officers |
symbols: str |
Executives, titles, and compensation |
Discovery
| Tool | Arguments | Returns |
|---|---|---|
search_symbols |
query: str |
Matching stock symbols by company name or partial ticker |
symbols is a comma-separated string, such as "AAPL,GOOGL,MSFT". Statement frequency accepts "annual" or "quarterly".
Development
Install development dependencies:
pip install -e ".[dev]"
Run the offline test suite and linter:
pytest ruff check .
See CONTRIBUTING.md for the full contribution guide.
Data source
Data is sourced from Yahoo Finance through the unofficial yahooquery library. It is subject to Yahoo's terms and no API key is required.
License
MIT — see LICENSE.
Support
Please open a GitHub Issue.
Facts
- Kind
- MCP server
- Group
- Uncategorized
- Stars
- 14
- License
- MIT
- Language
- Python
- Last push
- 2026-08-08
- Forks
- 3
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