Which integrations are available for this server?

Provides a Python client interface for connecting to the SEC MCP server to access SEC EDGAR data programmatically through async API operations.

How do I use SEC MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@SEC MCP get the latest 10-K filing for Apple and extract key financial metrics" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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: SSE MCP 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