# Venture Portfolio Construction AI Agent Connect

> Model optimal venture capital portfolios using power law distributions.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_nsYhlVE5wzcFX2aURp8H51bvx4k4fpIyfUDTs9VH/ai-agent-connect
- **Tags:** portfolio, power-law, venture-capital, investment, returns

## Description

This MCP server provides advanced modeling tools for venture capital fund construction. It uses a power law framework to determine the optimal number of investments, initial check sizes, and expected fund returns. Users can utilize `calculate_portfolio_strategy` to define foundational allocations, `estimate_expected_returns` to predict fund value based on outlier rates, `analyze_concentration_risk` to manage single-company exposure, and `optimize_investment_count` to find the minimum number of companies needed to hit a target return multiple.

## Tools

### optimize_investment_count
Finds the ideal number of investments needed to reach a specific target fund return multiple

### calculate_portfolio_strategy
Determines the foundational allocation strategy, including the number of companies to back and the initial check size

### analyze_concentration_risk
Evaluates if the proposed strategy adheres to specific risk-management constraints regarding single-company exposure

### estimate_expected_returns
Predicts the total fund value based on the power law distribution and return expectations

## Prompt Examples

**Prompt:** 
```
Calculate a strategy for a $100M fund with 20 target companies, a 60% failure rate, and a 10% home run rate.
```

**Response:** 
```
For a $100M fund targeting 20 companies, the initial check size is $4,000,000 if no reserves are held, or less if follow-on reserves are specified.
```

**Prompt:** 
```
What is the expected return for a $50M fund with 30 companies, a 5% home run rate, and a 50x multiplier?
```

**Response:** 
```
The expected fund value is $75,000,000, representing a 1.5x multiple.
```

**Prompt:** 
```
How many companies do I need to back to hit a 3x return on a $20M fund with a 50x home run multiplier and 5% home run rate?
```

**Response:** 
```
To achieve a 3x return, you would need to back at least 12 companies.
```

## Frequently Asked Questions

**How does the model account for fund returns?**
The model uses a power law distribution where returns are driven by a small number of 'home run' companies. You can use `estimate_expected_returns` to calculate the expected fund value based on your specific home run rate and multiplier.

**Can I check if my investment concentration is too high?**
Yes, the `analyze_concentration_risk` tool evaluates if your initial check size and planned follow-on reserves stay within your defined concentration limits.

**How do I find the right number of companies to invest in?**
You can use `optimize_investment_count` to determine the minimum number of investments required to reach a specific target return multiple.