# Analyze VC Fund Performance by Vintage Year AI Agent Connect

> Accelerator Vintage Year Performance helps venture capital and accelerator funds evaluate investment cohorts. This MCP provides specialized tools to score vintage years, compare performance against historical market benchmarks, and assess the maturity and liquidity profile of your investments. You can connect this MCP once from Claude, Cursor, Windsurf, or any MCP-compatible client to start running deep financial analysis immediately.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_tiTkZIrCxh1a0wtkQW9nqeU6sOAGLT00qzRNnQoy/ai-agent-connect
- **Tags:** vintage-year, dpi, tvpi, irr, fund-performance

## Description

Need to evaluate how well a specific investment cohort is performing? This MCP gives VC and accelerator funds the tools they need to analyze historical performance metrics. You can use it to get a normalized health score for any vintage year, measure its performance against standard market expectations, or assess its current lifecycle and liquidity profile. It takes complex, multi-variable data and turns it into clear, actionable insights. Just connect your AI client through Vinkius, and you can start running these deep analyses without needing to build custom data pipelines. It’s built for fund managers who need fast, accurate performance data.

## Tools

### analyze_cohort_maturity
Use this tool to assess the lifecycle status and liquidity profile of an accelerator cohort. It tells you where the cohort stands relative to its expected development stage.

### compare_to_benchmarks
This tool evaluates how a specific vintage cohort performs relative to standard market expectations. You can see if it's above or below the historical average.

### get_vintage_performance_score
Get a single, normalized health score for a specific investment cohort. This gives you one quick metric to gauge the overall health of the vintage year.

## Prompt Examples

**Prompt:** 
```
What is the performance score for a 2018 vintage with 1.5 DPI, 3.0 TVPI, and 15% IRR?
```

**Response:** 
```
The performance score for the 2018 vintage is 2.1, which is rated as Average.
```

**Prompt:** 
```
How does a 2020 vintage with 2.5 TVPI and 12% IRR compare to benchmarks?
```

**Response:** 
```
The 2020 vintage is performing 15% above the historical benchmark and is positioned in the Top Quartile.
```

**Prompt:** 
```
Analyze the maturity of a cohort with 50 companies, 5 exits, 40 active, and 0.2 DPI.
```

**Response:** 
```
The cohort is in the Early Stage with a low liquidity profile. The expected next phase is increased exit activity.
```

## Capabilities

### Score Vintage Health
Use the performance score tool when you need a single, normalized metric for an entire investment cohort.

### Benchmark Performance
Run a comparison when you need to know if a cohort is performing above or below standard market expectations.

### Assess Maturity
Check the lifecycle status and liquidity profile when you need to understand the cohort's current stage.

### Analyze DPI/TVPI
Input key metrics like DPI and TVPI to get a detailed assessment of the fund's financial standing.

## Use Cases

### Evaluating a New Fund
Before investing, run the performance score to see how a new vintage year stacks up against historical averages.

### Assessing Cohort Risk
Use the maturity analysis to identify cohorts that are stalled or have low liquidity, signaling potential risk.

### Quarterly Performance Review
Run benchmark comparisons to generate reports showing exactly where your portfolio stands relative to the industry standard.

### Identifying Next Milestones
Determine the expected next phase of a cohort, helping you plan follow-on capital or exit strategies.

## Benefits

- It reduces the time spent calculating complex, multi-variable performance metrics.
- You get immediate risk assessment by measuring a cohort's liquidity profile.
- It provides standardized scoring, allowing for direct, apples-to-apples comparisons across different funds.
- You don't need to build custom data pipelines; just connect your AI client and run the analysis.

## How It Works

Connecting is simple. You connect your preferred AI client to the Vinkius catalog, and the MCP becomes available to your agent. You then prompt your AI client with the specific vintage year data you want to analyze.

1. Connect your AI client (Claude, Cursor, etc.) to the Vinkius catalog.
2. Reference this MCP in your prompt, providing the vintage year details (DPI, TVPI, IRR, etc.).
3. Your AI client selects the appropriate tool, like `get_vintage_performance_score`.
4. The MCP runs the calculation and sends back a clear, actionable performance metric.

## Frequently Asked Questions

**What kind of data does this MCP analyze?**
It analyzes key financial metrics for investment cohorts, including DPI (Distributions to Paid-In Capital), TVPI (Total Value to Paid-In Capital), and IRR (Internal Rate of Return). You must provide these metrics in your prompt.

**Is this tool for all types of funds?**
No. This MCP is specialized for venture capital and accelerator funds that track investments by vintage year. It's designed for evaluating investment cohorts, not general corporate finance.

**Do I need to connect a database?**
No. You don't need to manage credentials or connect a database. You simply provide the specific data points (like the year, DPI, and TVPI) directly in your prompt to the MCP.

**What does 'normalized health score' mean?**
It means the MCP calculates a single, standardized number that summarizes the overall financial health of the cohort. This score lets you quickly compare different years on a single scale.

**Can I use this with my custom AI agent?**
Yes. As long as your agent is MCP-compatible, you can connect it through Vinkius and access this full suite of analytical tools.