PitchLense MCP - Professional Startup Risk Analysis Package

A comprehensive Model Context Protocol (MCP) package for analyzing startup investment risks using AI-powered assessment across multiple risk categories. Built with FastMCP and Google Gemini AI.

🚀 Features

Individual Risk Analysis Tools

Market Risk Analyzer - TAM, growth rate, competition, differentiation
Product Risk Analyzer - Development stage, market fit, technical feasibility, IP protection
Team Risk Analyzer - Leadership depth, founder stability, skill gaps, credibility
Financial Risk Analyzer - Metrics consistency, burn rate, projections, CAC/LTV
Customer Risk Analyzer - Traction levels, churn rate, retention, customer concentration
Operational Risk Analyzer - Supply chain, GTM strategy, efficiency, execution
Competitive Risk Analyzer - Incumbent strength, entry barriers, defensibility
Legal Risk Analyzer - Regulatory environment, compliance, legal disputes
Exit Risk Analyzer - Exit pathways, sector activity, late-stage appeal

Comprehensive Analysis Tools & Data Sources

Comprehensive Risk Scanner - Full analysis across all risk categories
Quick Risk Assessment - Fast assessment of critical risk areas
Peer Benchmarking - Compare metrics against sector/stage peers
SerpAPI Google News Tool - Fetches first-page Google News with URLs and thumbnails
Perplexity Search Tool - Answers with cited sources and URLs

📊 Risk Categories Covered

| Category | Key risks | | --- | --- | | Market | Small/overstated TAM; weak growth; crowded space; limited differentiation; niche dependence | | Product | Early stage; unclear PMF; technical uncertainty; weak IP; poor scalability | | Team/Founder | Single-founder risk; churn; skill gaps; credibility; misaligned incentives | | Financial | Inconsistent metrics; high burn/short runway; optimistic projections; unfavorable CAC/LTV; low margins | | Customer & Traction | Low traction; high churn; low retention; no marquee customers; concentration risk | | Operational | Fragile supply chain; unclear GTM; operational inefficiency; poor execution | | Competitive | Strong incumbents; low entry barriers; weak defensibility; saturation | | Legal & Regulatory | Grey/untested areas; compliance gaps; disputes; IP risks | | Exit | Unclear pathways; low sector exit activity; weak late‑stage appeal |

🛠️ Installation

From PyPI (Recommended)

pip install pitchlense-mcp

From Source

git clone https://github.com/pitchlense/pitchlense-mcp.git
cd pitchlense-mcp
pip install -e .

Development Installation

git clone https://github.com/pitchlense/pitchlense-mcp.git
cd pitchlense-mcp
pip install -e ".[dev]"

🔑 Setup

1. Get Gemini API Key

Visit Google AI Studio
Create a new API key
Copy the API key

2. Create .env

cp .env.template .env
# edit .env and fill in keys

Supported variables:

GEMINI_API_KEY=
SERPAPI_API_KEY=
PERPLEXITY_API_KEY=

🚀 Usage

Command Line Interface

Run Comprehensive Analysis

# Create sample data
pitchlense-mcp sample --output my_startup.json

# Run comprehensive analysis
pitchlense-mcp analyze --input my_startup.json --output results.json

Run Quick Assessment

pitchlense-mcp quick --input my_startup.json --output quick_results.json

Start MCP Server

pitchlense-mcp server

Python API

Basic Usage (single text input)

from pitchlense_mcp import ComprehensiveRiskScanner

# Initialize scanner (reads GEMINI_API_KEY from env if not provided)
scanner = ComprehensiveRiskScanner()

# Provide all startup info as one organized text string
startup_info = """
Name: TechFlow Solutions
Industry: SaaS/Productivity Software
Stage: Series A

Business Model:
AI-powered workflow automation for SMBs; subscription pricing.

Financials:
MRR: $45k; Burn: $35k; Runway: 8 months; LTV/CAC: 13.3

Traction:
250 customers; 1,200 MAU; Churn: 5% monthly; NRR: 110%

Team:
CEO: Sarah Chen; CTO: Michael Rodriguez; Team size: 12

Market & Competition:
TAM: $12B; Competitors: Zapier, Power Automate; Growth: 15% YoY
"""

# Run comprehensive analysis
results = scanner.comprehensive_startup_risk_analysis(startup_info)

print(f"Overall Risk Level: {results['overall_risk_level']}")
print(f"Overall Risk Score: {results['overall_score']}/10")
print(f"Investment Recommendation: {results['investment_recommendation']}")

Individual Risk Analysis (text input)

from pitchlense_mcp import MarketRiskAnalyzer, GeminiLLM

# Initialize components
llm_client = GeminiLLM(api_key="your_api_key")
market_analyzer = MarketRiskAnalyzer(llm_client)

# Analyze market risks
market_results = market_analyzer.analyze(startup_info)
print(f"Market Risk Level: {market_results['overall_risk_level']}")

MCP Server Integration

The package provides a complete MCP server that can be integrated with MCP-compatible clients:

from pitchlense_mcp import ComprehensiveRiskScanner

# Start MCP server
scanner = ComprehensiveRiskScanner()
scanner.run()

📋 Input Data Format

The primary input is a single organized text string containing all startup information (details, metrics, traction, news, competitive landscape, etc.). This is the format used by all analyzers and MCP tools.

Example text input:

Name: AcmeAI
Industry: Fintech (Lending)
Stage: Seed

Summary:
Building AI-driven credit risk models for SMB lending; initial pilots with 5 lenders.

Financials:
MRR: $12k; Burn: $60k; Runway: 10 months; Gross Margin: 78%

Traction:
200 paying SMBs; 30% MoM growth; Churn: 3% monthly; CAC: $220; LTV: $2,100

Team:
Founders: Jane Doe (ex-Square), John Lee (ex-Stripe); Team size: 9

Market & Competition:
TAM: $25B; Competitors: Blend, Upstart; Advantage: faster underwriting via proprietary data partnerships

Tip: See examples/text_input_example.py for a complete end-to-end script and JSON export of results.

📊 Output Format

All tools return structured JSON responses with:

{
    "startup_name": "Startup Name",
    "overall_risk_level": "low|medium|high|critical",
    "overall_score": 1-10,
    "risk_categories": [
        {
            "category_name": "Risk Category",
            "overall_risk_level": "low|medium|high|critical",
            "category_score": 1-10,
            "indicators": [
                {
                    "indicator": "Specific risk factor",
                    "risk_level": "low|medium|high|critical",
                    "score": 1-10,
                    "description": "Detailed risk description",
                    "recommendation": "Mitigation action"
                }
            ],
            "summary": "Category summary"
        }
    ],
    "key_concerns": ["Top 5 concerns"],
    "investment_recommendation": "Investment advice",
    "confidence_score": 0.0-1.0,
    "analysis_metadata": {
        "total_categories_analyzed": 9,
        "successful_analyses": 9,
        "analysis_timestamp": "2024-01-01T00:00:00Z"
    }
}

🎯 Use Cases

Investor Due Diligence - Comprehensive risk assessment for investment decisions
Startup Self-Assessment - Identify and mitigate key risk areas
Portfolio Risk Management - Assess risk across startup portfolio
Accelerator/Incubator Screening - Evaluate startup applications
M&A Risk Analysis - Assess acquisition targets
Research & Analysis - Academic and industry research on startup risks

🏗️ Architecture

Package Structure

pitchlense-mcp/
├── pitchlense_mcp/
│   ├── __init__.py
│   ├── cli.py                 # Command-line interface
│   ├── core/                  # Core functionality
│   │   ├── __init__.py
│   │   ├── base.py           # Base classes
│   │   ├── gemini_client.py  # Gemini AI integration
│   │   └── comprehensive_scanner.py
│   ├── models/               # Data models
│   │   ├── __init__.py
│   │   └── risk_models.py
│   ├── analyzers/            # Individual risk analyzers
│   │   ├── __init__.py
│   │   ├── market_risk.py
│   │   ├── product_risk.py
│   │   ├── team_risk.py
│   │   ├── financial_risk.py
│   │   ├── customer_risk.py
│   │   ├── operational_risk.py
│   │   ├── competitive_risk.py
│   │   ├── legal_risk.py
│   │   └── exit_risk.py
│   └── utils/                # Utility functions
├── tests/                    # Test suite
├── docs/                     # Documentation
├── examples/                 # Example usage
├── setup.py
├── pyproject.toml
├── requirements.txt
└── README.md

Key Components

Base Classes (core/base.py)
- BaseLLM - Abstract base for LLM integrations
- BaseRiskAnalyzer - Base class for all risk analyzers
- BaseMCPTool - Base class for MCP tools
Gemini Integration (core/gemini_client.py)
- GeminiLLM - Main LLM client
- GeminiTextGenerator - Text generation
- GeminiImageAnalyzer - Image analysis
- GeminiVideoAnalyzer - Video analysis
- GeminiAudioAnalyzer - Audio analysis
- GeminiDocumentAnalyzer - Document analysis
Risk Analyzers (analyzers/)
- Individual analyzers for each risk category
- Consistent interface and output format
- Extensible architecture
Models (models/risk_models.py)
- Pydantic models for type safety
- Structured data validation
- Clear data contracts

🔧 Development

Setup Development Environment

git clone https://github.com/pitchlense/pitchlense-mcp.git
cd pitchlense-mcp
pip install -e ".[dev]"
pre-commit install

Run Tests

# Create and activate a virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate

# Install dev extras (pytest, pytest-cov, linters)
pip install -e ".[dev]"

# Run tests with coverage and avoid global plugin conflicts
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 pytest -q -p pytest_cov

Notes:

Coverage reports are written to htmlcov/index.html and coverage.xml.
If you see errors about unknown --cov options, ensure you passed -p pytest_cov when PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 is set.

Example Scripts

python examples/basic_usage.py
python examples/text_input_example.py

Code Formatting

black pitchlense_mcp/
flake8 pitchlense_mcp/
mypy pitchlense_mcp/

Build Package

python -m build

📝 Notes

All risk scores are on a 1-10 scale (1 = lowest risk, 10 = highest risk)
Risk levels: low (1-3), medium (4-6), high (7-8), critical (9-10)
Individual tools can be used independently or combined for comprehensive analysis
The system handles API failures gracefully with fallback responses
All tables and structured data are returned in JSON format
Professional package architecture with proper separation of concerns

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Fork the repository
Create a feature branch
Make your changes
Add tests
Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Documentation: https://pitchlense-mcp.readthedocs.io/
Issues: GitHub Issues
Email: connectamanulla@gmail.com

🙏 Acknowledgments

Google Gemini AI for providing the underlying AI capabilities
FastMCP for the Model Context Protocol implementation
The open-source community for inspiration and tools

PitchLense MCP - Making startup risk analysis accessible, comprehensive, and AI-powered.