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.
8 Specialized MCPs • PhD-Level Analysis • Institutional Quality
🎓 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:
| MCP | Description | Key Features |
|---|---|---|
| SEC Scraper | XBRL parsing & comprehensive analysis | DCF modeling, Monte Carlo simulations |
| News Sentiment | Advanced NLP for financial text | Context-aware sentiment, earnings call analysis |
| Analyst Ratings | Consensus tracking & peer comparison | Rating aggregation, price target analysis |
| Institutional | Ownership & fund flow analysis | 13F tracking, insider transactions |
| Alternative Data | Web scraping for unique insights | Hiring trends, social sentiment, reviews |
| Industry Assumptions | Sector analysis & modeling | WACC calculations, peer metrics |
| Economic Data | Macro indicators & regime detection | Fed data, employment, inflation |
| Research Admin | Report generation & orchestration | 25+ page institutional reports |
🚀 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
- Clone the repository:
git clone https://github.com/yourusername/financial-mcps.git
cd financial-mcps
- Create and activate virtual environment:
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
- Install dependencies:
uv sync
- 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
- 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.