Financial MCPs - PhD-Level Research Tools for Claude Code CLI

A comprehensive collection of advanced Model Context Protocol (MCP) servers that transform Claude Code CLI into an institutional-grade financial research platform.

🎓 Overview

This repository contains 8 specialized MCP servers that provide Claude Code CLI with capabilities rivaling professional financial platforms used by hedge funds and investment banks:

🚀 Features

Advanced Financial Analysis

• XBRL Parsing: Extract 50+ structured metrics from SEC filings
• DCF Valuation: Monte Carlo simulations with 10,000 iterations
• Financial Metrics: ROE, ROIC, Altman Z-Score, Piotroski F-Score
• Peer Comparison: Automatic competitor identification and analysis

Market Intelligence

• PhD-Level NLP: Context-aware sentiment analysis for earnings calls
• Technical Analysis: RSI, MACD, Bollinger Bands, support/resistance
• Market Regime Detection: Bull/bear market identification
• Sector Rotation: Industry trend and momentum analysis

Research Output

• Institutional Reports: Professional 25+ page equity research documents
• Investment Thesis: Comprehensive bull/bear cases with catalysts
• Risk Assessment: Multi-factor risk scoring and analysis
• Quality Metrics: Data completeness and confidence scoring

📦 Installation

Prerequisites

• Python 3.10+
• Claude Code CLI (npm install -g @anthropic-ai/claude-cli)
• uv package manager (pip install uv)

Quick Setup

1. Clone the repository:

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

2. Create and activate virtual environment:

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

3. Install dependencies:

uv sync

4. Add all MCPs to Claude Code CLI:

# 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

5. Verify installation:

claude mcp list
# Should show all 8 Financial MCPs

💡 Usage Examples

Basic Commands

# 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"

Advanced Analysis

# 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"

Professional Workflows

Investment Research Workflow

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

Risk Assessment Workflow

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

🏗️ Architecture

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

🔧 Configuration

MCP-Specific Settings

Each MCP can be configured through environment variables:

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

Analysis Parameters

Edit analysis_config in each MCP's main.py:

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
}

Cache Settings

Configure cache TTL in shared/data_cache.py:

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

🧪 Testing

Run All Tests

python test_phd_features.py

Test Individual MCPs

./test_single_mcp.sh SEC_SCRAPER_MCP

Debug Mode

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

📊 Data Sources

• SEC EDGAR: Official filings, XBRL data
• Yahoo Finance: Real-time prices, basic metrics
• Finviz: News aggregation, analyst ratings
• MarketWatch: Additional market data
• Federal Reserve: Economic indicators
• Alternative Sources: Indeed, Glassdoor, Reddit, Google Trends

🔒 Security & Compliance

• Rate Limiting: Built-in delays to respect data source limits
• User Agent: Proper identification for web scraping
• Caching: Reduces redundant requests
• Data Validation: Ensures data quality and accuracy

⚠️ Disclaimer

These tools are for educational and research purposes only. Not intended for:

• Production trading systems
• Real money investment decisions
• High-frequency trading
• Regulatory compliance

Always verify data independently and conduct your own due diligence.

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for:

• Code style guidelines
• Testing requirements
• Pull request process
• Feature request procedure

📈 Roadmap

• Bloomberg/Refinitiv data integration
• Real-time streaming capabilities
• Machine learning predictions
• Options analytics
• Portfolio optimization
• Backtesting framework

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Built for Claude Code CLI by Anthropic
• Inspired by institutional research platforms
• Uses publicly available financial data sources
• Special thanks to the MCP community

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki

Note: This is an advanced financial research toolkit. Users should have a solid understanding of financial analysis and Python programming. These MCPs provide PhD-level analysis capabilities previously only available to institutional investors.