stock-analyst-mcp

MCP server for global stock market analysis — fundamentals, technicals, DCF valuation, peer comparison, multi-asset support, and more. Works for 50+ regions worldwide.

What's New in v0.5.0

Performance Overhaul — 3-19x faster peer analysis

• Parallel peer fundamentals: ThreadPoolExecutor on get_info() calls (3.7x speedup)
• Batch history downloads: Single yf.download() for all peers (19.3x speedup)
• Parallel snippet fetching: News analysis now fetches article snippets concurrently
• New stock_analyst/utils/ module: Reusable concurrency helpers (parallel_map, parallel_map_dict, batch_download_history)
• Zero new dependencies: Uses stdlib concurrent.futures

Example: Analyzing LOW (US) with 10 peers now takes ~2-3s instead of 8-10s.

Install

pip install stock-analyst-mcp

Or run directly without installing:

uvx stock-analyst-mcp

MCP Configuration

Add to your MCP client config (Claude Desktop, Devin, Cursor, etc.):

{
  "mcpServers": {
    "stock-analyst": {
      "command": "uvx",
      "args": ["stock-analyst-mcp"]
    }
  }
}

Or if installed via pip:

{
  "mcpServers": {
    "stock-analyst": {
      "command": "stock-analyst-mcp"
    }
  }
}

Tools

Configuration Tools

get_config

Retrieve all current configuration settings. Useful for understanding what parameters are available before calling set_config.

from stock_analyst import get_config

config = get_config()
# Returns dict with sections:
# - data_provider, default_exchange, default_period, cache settings
# - technical_analysis: EMA periods, RSI period, MACD params, Bollinger settings
# - financial_analysis: DCF params, WACC settings, forecast scenarios
# - peer_comparison: max count, metrics to compare
# - output: format, pretty-print settings

set_config

Update configuration dynamically without restarting. Changes affect subsequent tool calls.

from stock_analyst import set_config

# Change technical analysis period from 1y to 1d
result = set_config("default_period", "1d")
# Returns: {"status": "success", "key": "default_period", "new_value": "1d", "affected_tools": ["all_tools"]}

# Change RSI period from 14 to 21
result = set_config("ta_rsi_period", "21")
# Returns: {"status": "success", "key": "ta_rsi_period", "new_value": 21, "affected_tools": ["get_technicals", "analyze_stock"]}

# Change DCF projection years from 5 to 10
result = set_config("fa_dcf_projection_years", "10")
# Returns: {"status": "success", "key": "fa_dcf_projection_years", "new_value": 10, "affected_tools": ["get_dcf_valuation", "get_revenue_forecast", "analyze_stock"]}

Common Configuration Keys:

Example: Customize Technical Analysis

from stock_analyst import set_config, get_technicals

# Use 1-day data with custom RSI period
set_config("default_period", "1d")
set_config("ta_rsi_period", "21")

# Get technicals with new settings
signals = get_technicals("RELIANCE")

Example: Customize DCF Valuation

from stock_analyst import set_config, get_dcf_valuation

# Use 10-year projection with different growth assumptions
set_config("fa_dcf_projection_years", "10")
set_config("fa_dcf_terminal_growth", "0.03")  # 3% terminal growth
set_config("fa_wacc_risk_free_rate", "0.065")  # 6.5% risk-free rate

# Get DCF with new assumptions
valuation = get_dcf_valuation("RELIANCE")

CLI

Also works as a standalone CLI (no LLM needed):

# Full analysis
stock-analyst --symbol RELIANCE

# Specific analysis
stock-analyst --symbol TCS --analysis fundamentals
stock-analyst --symbol INFY --analysis technicals
stock-analyst --symbol RELIANCE --analysis dcf

# Compare multiple stocks
stock-analyst --symbols RELIANCE,TCS,INFY --compare

# Markdown output
stock-analyst --symbol RELIANCE --format markdown

# Raw data
stock-analyst --symbol RELIANCE --raw financials

# Market mood (no symbol needed)
stock-analyst --analysis market-mood

# Stock screener (India)
stock-analyst --screen --sector Technology --pe-max 30 --roe-min 0.15
stock-analyst --screen --market-cap-min 50000000000 --sort-by pe --limit 20

# Global stocks (any region)
stock-analyst --symbol AAPL --region us
stock-analyst --symbol 0700.HK --region hk
stock-analyst --screen --region gb --sector Technology --pe-max 25

# Market mood (global)
stock-analyst --analysis market-mood --region us
stock-analyst --analysis market-mood --region de

# Ticker search
stock-analyst --search "Apple" --search-type stock --region us
stock-analyst --search "Bitcoin" --search-type cryptocurrency

# Multi-asset analysis
stock-analyst --symbol SPY --analysis asset --asset-type etf
stock-analyst --symbol GC=F --analysis asset --asset-type commodity
stock-analyst --symbol BTC-USD --analysis asset --asset-type crypto

Configuration

All settings configurable via environment variables with SA_ prefix. Defaults work out of the box for Indian markets (NSE). Supports 50+ regions globally.

See configurations.env.example for the full list.

Python Library

from stock_analyst import (
    analyze, get_fundamentals, get_technicals,
    get_news, get_market_mood, screen_stocks,
    search_tickers, analyze_asset,
)

# Indian stocks (default region)
result = analyze("RELIANCE")
ratios = get_fundamentals("TCS")
signals = get_technicals("INFY", period="6mo")

# Global stocks (any region)
us_stock = analyze("AAPL", region="us")
hk_stock = analyze("0700.HK", region="hk")
uk_stock = analyze("HSBA", region="gb")

# News with sentiment
news = get_news("TCS")
# Returns headlines with sentiment_score, sentiment_label, snippet

# Market mood (region-specific)
mood_in = get_market_mood(region="in")  # Includes MMI from tickertape
mood_us = get_market_mood(region="us")  # S&P 500 + VIX
mood_de = get_market_mood(region="de")  # DAX + VDAX

# Stock screener (any region)
results_in = screen_stocks({"sector": "Technology", "pe_max": 30}, region="in")
results_us = screen_stocks({"sector": "Technology", "pe_max": 40}, region="us")

# Ticker search
apple_results = search_tickers("Apple", instrument_type="stock", region="us")
crypto_results = search_tickers("Bitcoin", instrument_type="cryptocurrency")

# Multi-asset analysis
etf = analyze_asset("SPY", asset_type="etf")
commodity = analyze_asset("GC=F", asset_type="commodity")
crypto = analyze_asset("BTC-USD", asset_type="crypto")
currency = analyze_asset("EURUSD=X", asset_type="currency")

Testing

# Install dev dependencies
pip install -e ".[dev]"

# Run all tests
pytest

# Run with coverage
pytest --cov=stock_analyst --cov-report=term-missing

# Run specific test file
pytest tests/test_peers.py -v

Data Sources

• yfinance — OHLCV, financials, balance sheet, cashflow, info, peer discovery via Industry API, stock screener via EquityQuery (50+ regions)
• screener.in — peer discovery + stock screener fallback for India (best-effort, graceful degradation)
• tickertape.in — Market Mood Index (MMI) scraping for India
• VADER — headline sentiment analysis (vaderSentiment)
• India-adjusted defaults — risk-free rate 7%, cost of debt 9%, tax 25%

Supported Regions

50+ regions via yfinance: US, UK, Germany, France, Italy, Spain, Netherlands, Belgium, Switzerland, Austria, Sweden, Norway, Denmark, Finland, Poland, Czech Republic, Romania, Portugal, Greece, Hungary, Ireland, Lithuania, Latvia, Estonia, Canada, Mexico, Brazil, Argentina, Chile, Peru, Colombia, Venezuela, Australia, New Zealand, Japan, South Korea, China, Hong Kong, Singapore, Malaysia, Thailand, Philippines, Indonesia, Vietnam, Pakistan, Sri Lanka, UAE, Saudi Arabia, Kuwait, Qatar, Israel, Egypt, Turkey, South Africa, and more.

Regions Quick Reference

License

MIT