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.

MCP Protocol Claude Code Python License

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

Related MCP server: MCP SSE Server

🚀 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.

SEC MCP