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SEC MCP

by LuisRincon23
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MIT License
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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

🚀 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

  1. Create and activate virtual environment:

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

  1. Install dependencies:

uv sync

  1. 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

  1. 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

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.

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A Server-Sent Events Model Context Protocol server that enables both remote and local connections to retrieve SEC filing data, company information, and financial facts from the SEC EDGAR database.

  1. 🎓 Overview
    1. 🚀 Features
      1. Advanced Financial Analysis
      2. Market Intelligence
      3. Research Output
    2. 📦 Installation
      1. Prerequisites
      2. Quick Setup
    3. 💡 Usage Examples
      1. Basic Commands
      2. Advanced Analysis
      3. Professional Workflows
    4. 🏗️ Architecture
      1. 🔧 Configuration
        1. MCP-Specific Settings
        2. Analysis Parameters
        3. Cache Settings
      2. 🧪 Testing
        1. Run All Tests
        2. Test Individual MCPs
        3. Debug Mode
      3. 📊 Data Sources
        1. 🔒 Security & Compliance
          1. ⚠️ Disclaimer
            1. 🤝 Contributing
              1. 📈 Roadmap
                1. 📄 License
                  1. 🙏 Acknowledgments
                    1. 📞 Support

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