# Mining Project Risk Analysis AI Agent Connect

> Perform Monte Carlo simulations to assess financial risk and uncertainty in mining projects.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_VlIrOPUWXdaE16SuH555kaTezIgYK3j8h1bIXIuH/ai-agent-connect
- **Tags:** monte-carlo, npv, risk-analysis, mining, probabilistic-modeling

## Description

This MCP server provides advanced probabilistic modeling for mining project evaluation. It allows AI agents to execute Monte Carlo simulations to determine the distribution of Net Present Value (NPV) outcomes. By modeling parameter distributions and correlations, users can calculate critical confidence levels like P10, P50, and P90, and determine the probability of loss. The server includes tools to `run_monte_carlo_simulation` for core modeling, `get_parameter_summary` for statistical overviews, `validate_correlation_matrix` to ensure mathematical consistency, and `calculate_sensitivity_index` to identify key risk drivers.

## Tools

### get_parameter_summary
Provides a descriptive statistical overview of the input parameters

### run_monte_carlo_simulation
Executes the core simulation engine to determine the distribution of project outcomes

### validate_correlation_matrix
Checks if a set of proposed correlations is mathematically consistent and valid

### calculate_sensitivity_index
Identifies which input variables have the greatest impact on the variance of the NPV

## Prompt Examples

**Prompt:** 
```
Run a simulation with 1000 iterations for a project where metal price is lognormal (mean 80, std 5) and ore grade is normal (mean 2, std 0.2).
```

**Response:** 
```
The simulation results show a P50 NPV of $450M, a P90 of $320M, and a 5% probability of loss.
```

**Prompt:** 
```
What is the statistical summary for these parameters: [{"name": "price", "type": "lognormal", "mean": 100, "std": 10}]?
```

**Response:** 
```
The parameter 'price' has a mean of 100 and a standard deviation of 10.
```

**Prompt:** 
```
Check if these correlations are valid for parameters 'price' and 'cost': [{"param1": "price", "param2": "cost", "coefficient": 0.8}]
```

**Response:** 
```
The correlation matrix is valid and mathematically consistent.
```

## Frequently Asked Questions

**What is a Monte Carlo simulation in this context?**
It is a technique that repeatedly samples values from probability distributions for uncertain input parameters to generate a range of possible NPV outcomes.

**How do I know which parameters impact my project most?**
You can use the `calculate_sensitivity_index` tool to identify which input variables have the greatest impact on the variance of the NPV.

**Can I model dependencies between variables?**
Yes, you can provide correlations between parameters. Use `validate_correlation_matrix` first to ensure your proposed correlations are mathematically valid.