git clone https://github.com/yourusername/financial-mcps.git
cd financial-mcps

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

uv sync

# Run the setup script
./setup_all_mcps.sh

# Or manually add each MCP:
claude mcp add SEC "./FinancialMCPs/SEC_SCRAPER_MCP/start-mcp.sh" --transport stdio
claude mcp add NEWS-SENTIMENT "./FinancialMCPs/NEWS_SENTIMENT_SCRAPER/start-mcp.sh" --transport stdio
claude mcp add ANALYST-RATINGS "./FinancialMCPs/ANALYST_RATINGS_SCRAPER/start-mcp.sh" --transport stdio
claude mcp add INSTITUTIONAL "./FinancialMCPs/INSTITUTIONAL_SCRAPER/start-mcp.sh" --transport stdio
claude mcp add ALTERNATIVE-DATA "./FinancialMCPs/ALTERNATIVE_DATA_SCRAPER/start-mcp.sh" --transport stdio
claude mcp add INDUSTRY-ASSUMPTIONS "./FinancialMCPs/INDUSTRY_ASSUMPTIONS_ENGINE/start-mcp.sh" --transport stdio
claude mcp add ECONOMIC-DATA "./FinancialMCPs/ECONOMIC_DATA_COLLECTOR/start-mcp.sh" --transport stdio
claude mcp add RESEARCH-ADMIN "./FinancialMCPs/RESEARCH_ADMINISTRATOR/start-mcp.sh" --transport stdio

claude mcp list
# Should show all 8 Financial MCPs

# Get current stock price
Use SEC to get current price for ticker "AAPL"

# Analyze sentiment
Use NEWS-SENTIMENT to analyze sentiment for ticker "MSFT"

# Get analyst consensus
Use ANALYST-RATINGS to get consensus rating for ticker "GOOGL"

# Comprehensive stock analysis (PhD-level)
Use SEC to perform comprehensive analysis for ticker "NVDA"

# Generate institutional research report
Use RESEARCH-ADMIN to generate research report for ticker "TSLA"

# Sector analysis
Use INDUSTRY-ASSUMPTIONS to analyze sector "Technology"

1. Use SEC to perform comprehensive analysis for ticker "META"
2. Use NEWS-SENTIMENT to analyze earnings call sentiment for ticker "META"  
3. Use ANALYST-RATINGS to compare with peer ratings
4. Use RESEARCH-ADMIN to generate investment thesis

1. Use SEC to calculate Altman Z-Score for ticker "GME"
2. Use INSTITUTIONAL to track ownership changes
3. Use ECONOMIC-DATA to assess macro risks
4. Use ALTERNATIVE-DATA to gauge social sentiment

financial-mcps/
├── FinancialMCPs/
│   ├── SEC_SCRAPER_MCP/           # XBRL parsing, DCF modeling
│   ├── NEWS_SENTIMENT_SCRAPER/    # Advanced NLP sentiment
│   ├── ANALYST_RATINGS_SCRAPER/   # Consensus tracking
│   ├── INSTITUTIONAL_SCRAPER/     # Ownership analysis
│   ├── ALTERNATIVE_DATA_SCRAPER/  # Web scraping
│   ├── INDUSTRY_ASSUMPTIONS/      # Sector analysis
│   ├── ECONOMIC_DATA_COLLECTOR/   # Macro indicators
│   ├── RESEARCH_ADMINISTRATOR/    # Report generation
│   └── shared/                    # Shared advanced modules
│       ├── financial_analysis.py  # DCF, metrics calculations
│       ├── xbrl_parser.py        # XBRL data extraction
│       ├── advanced_nlp.py       # PhD-level NLP
│       ├── research_report_generator.py
│       └── data_cache.py         # Intelligent caching
├── setup_all_mcps.sh             # Quick setup script
├── test_phd_features.py          # Integration tests
├── requirements.txt
├── README.md
└── LICENSE

export CACHE_DIR="/tmp/financial_mcp_cache"
export LOG_LEVEL="INFO"
export RATE_LIMIT_DELAY="1.0"  # SEC compliance

self.analysis_config = {
    'dcf_years': 5,              # DCF projection years
    'peer_count': 10,            # Number of peers to analyze
    'monte_carlo_simulations': 10000,  # Simulation count
    'confidence_threshold': 0.8   # Minimum confidence score
}

self.ttl_config = {
    'price_data': timedelta(minutes=5),
    'financial_statements': timedelta(days=90),
    'news': timedelta(hours=1),
    'research_reports': timedelta(days=30)
}

python test_phd_features.py

./test_single_mcp.sh SEC_SCRAPER_MCP

claude --debug
# Then use any MCP command to see detailed logs