PitchLense

PitchLense MCP - Professional Startup Risk Analysis Package

🏆 WINNER !!! of Google Cloud Gen AI Exchange Hackathon under the problem statement “AI Analyst for Startup Evaluation.” 🏆 Competing among 278,000+ participants and 180,000+ teams nationwide

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.

PitchLense is a comprehensive AI-powered startup analysis platform that provides detailed risk assessment and growth potential evaluation for early-stage ventures. The platform analyzes multiple dimensions of startup risk and provides actionable insights for investors, founders, and stakeholders.

🔗 Quick Links

• Website Link : https://www.pitchlense.com
• Web App Github Repo: https://github.com/connectaman/PitchLense

📖 How to Use PitchLense

Watch our comprehensive tutorial video to learn how to use PitchLense effectively:

Click the image above to watch the tutorial on YouTube

🚀 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

1. Visit Google AI Studio
2. Create a new API key
3. 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

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README truncated. View full README on GitHub.