# SEC MCP

> SEC MCP provides a streamlined interface to access SEC EDGAR data through the Model Context Protocol. It supports real-time data streaming via SSE, making it ideal for both local development and remote deployment scenarios. Built with Python and the MCP framework, this server enables seamless integration with AI assistants and other tools.

- **Type:** MCP server
- **Install:** `agentstack add mcp-luisrincon23-sec-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [LuisRincon23](https://agentstack.voostack.com/s/luisrincon23)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [LuisRincon23](https://github.com/LuisRincon23)
- **Source:** https://github.com/LuisRincon23/SEC-MCP

## Install

```sh
agentstack add mcp-luisrincon23-sec-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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.

[](https://modelcontextprotocol.io)
[](https://github.com/anthropics/claude-cli)
[](https://python.org)
[](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**:
```bash
git clone https://github.com/yourusername/financial-mcps.git
cd financial-mcps
```

2. **Create and activate virtual environment**:
```bash
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
```

3. **Install dependencies**:
```bash
uv sync
```

4. **Add all MCPs to Claude Code CLI**:
```bash
# 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**:
```bash
claude mcp list
# Should show all 8 Financial MCPs
```

## 💡 Usage Examples

### Basic Commands

```bash
# 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

```bash
# 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
```bash
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
```bash
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:
```bash
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:
```python
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`:
```python
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
```bash
python test_phd_features.py
```

### Test Individual MCPs
```bash
./test_single_mcp.sh SEC_SCRAPER_MCP
```

### Debug Mode
```bash
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](CONTRIBUTING.md) 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](LICENSE) file for details.

## 🙏 Acknowledgments

- Built for [Claude Code CLI](https://github.com/anthropics/claude-cli) by Anthropic
- Inspired by institutional research platforms
- Uses publicly available financial data sources
- Special thanks to the MCP community

## 📞 Support

- **Issues**: [GitHub Issues](https://github.com/yourusername/financial-mcps/issues)
- **Discussions**: [GitHub Discussions](https://github.com/yourusername/financial-mcps/discussions)
- **Documentation**: [Wiki](https://github.com/yourusername/financial-mcps/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.

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [LuisRincon23](https://github.com/LuisRincon23)
- **Source:** [LuisRincon23/SEC-MCP](https://github.com/LuisRincon23/SEC-MCP)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-luisrincon23-sec-mcp
- Seller: https://agentstack.voostack.com/s/luisrincon23
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.