Integrates with Google Gemini AI to power comprehensive startup risk analysis across multiple categories including market, product, team, financial, customer, operational, competitive, legal, and exit risks
Fetches Google News articles with URLs and thumbnails to gather market intelligence and news data for startup risk assessment
Provides search capabilities with cited sources and URLs to gather additional research data for startup analysis and risk evaluation
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)
From Source
Development Installation
🔑 Setup
1. Get Gemini API Key
- Visit Google AI Studio
- Create a new API key
- Copy the API key
2. Create .env
Supported variables:
🚀 Usage
Command Line Interface
Run Comprehensive Analysis
Run Quick Assessment
Start MCP Server
Python API
Basic Usage (single text input)
Individual Risk Analysis (text input)
MCP Server Integration
The package provides a complete MCP server that can be integrated with MCP-compatible clients:
📋 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:
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:
🎯 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
Key Components
- Base Classes (
core/base.py
)BaseLLM
- Abstract base for LLM integrationsBaseRiskAnalyzer
- Base class for all risk analyzersBaseMCPTool
- Base class for MCP tools
- Gemini Integration (
core/gemini_client.py
)GeminiLLM
- Main LLM clientGeminiTextGenerator
- Text generationGeminiImageAnalyzer
- Image analysisGeminiVideoAnalyzer
- Video analysisGeminiAudioAnalyzer
- Audio analysisGeminiDocumentAnalyzer
- 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
Run Tests
Notes:
- Coverage reports are written to
htmlcov/index.html
andcoverage.xml
. - If you see errors about unknown
--cov
options, ensure you passed-p pytest_cov
whenPYTEST_DISABLE_PLUGIN_AUTOLOAD=1
is set.
Example Scripts
Code Formatting
Build Package
📝 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.
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Enables comprehensive AI-powered startup investment risk analysis across 9 categories including market, product, team, financial, customer, operational, competitive, legal, and exit risks. Provides structured risk assessments, peer benchmarking, and investment recommendations using Google Gemini AI.