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Venture Portfolio Construction

Venture Portfolio Construction MCP Connector for Claude

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Model optimal venture capital portfolios using power law distributions.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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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.

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4 tools expose this connector's capabilities to your AI agent.

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

See how to talk to your AI agent using Venture Portfolio Construction.

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

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.

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

The expected fund value is $75,000,000, representing a 1.5x multiple.

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?

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

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.

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