# Exploration Success Probability Engine for geology. AI Agent Connect

> Exploration Success Probability Engine provides the math needed to assess risk in geological programs. It accounts for spatial and geological correlations between prospects to give you a realistic view of your portfolio. Your AI client uses these tools to calculate cumulative discovery chances and estimate the mathematical value of potential finds.

## Overview
- **Category:** mathematics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_x1buo60lVS1PDSyWl1GQnPxcT7qBaa3SWjbutKDD/ai-agent-connect
- **Tags:** geology, probability, resource-estimation, exploration, risk-modeling

## Description

You can stop guessing at the likelihood of a hit. This MCP gives your AI client the specific computational tools required to model risk and discovery probability within geological exploration programs. Instead of looking at prospects in isolation, it factors in the spatial and geological correlations between multiple sites to provide a more accurate picture of your total risk. 

When you're evaluating a portfolio, you can use it to determine the cumulative chance of finding at least one resource or to estimate the expected number of successful discoveries. It also handles volume-based risk, allowing you to apply probability to total potential volumes to find the risked resource value. If you need to see how much your model depends on specific assumptions, you can test how sensitive your discovery probability is to changes in geological correlation. It turns raw geological data into actionable mathematical models for your exploration strategy.

## Tools

### calculate_discovery_probability
This tool determines the cumulative likelihood of finding at least one resource across your entire exploration portfolio.

### calculate_expected_value
Use this to quantify the average number of successful discoveries you can expect from your current set of prospects.

### calculate_risked_resource
This tool estimates the risked volume of a resource by applying success probability to the total potential volume.

### analyze_correlation_sensitivity
This tool helps you understand how much your discovery probability changes when geological correlation values shift.

## Prompt Examples

**Prompt:** 
```
What is the probability of finding at least one resource if I have 5 prospects, each with a 20% success chance, and a correlation of 0.3?
```

**Response:** 
```
The probability of at least one discovery is 64.2%.
```

**Prompt:** 
```
Calculate the risked volume for a prospect with a 15% success probability and a total potential volume of 500 million cubic meters across 3 prospects.
```

**Response:** 
```
The risked volume is 225 million cubic meters.
```

**Prompt:** 
```
How many successful discoveries should I expect from 10 prospects with a 25% individual success probability?
```

**Response:** 
```
You can expect 2.5 successful discoveries.
```

## Capabilities

### Portfolio Risk Modeling
Your agent calculates the cumulative chance of success across multiple geological prospects.

### Resource Value Estimation
The AI applies probability to potential volumes to determine risked resource amounts.

### Correlation Analysis
Your client tests how sensitive discovery chances are to geological correlation changes.

### Expected Outcome Calculation
The tool provides the average number of expected successful discoveries in a program.

## Use Cases

### Portfolio Evaluation
Determine the total probability of finding a resource across a group of related prospects.

### Risked Volume Calculation
Apply success probabilities to total potential volumes to get a realistic resource estimate.

### Sensitivity Testing
Check how much your discovery risk changes if your geological correlation assumptions are slightly off.

### Discovery Forecasting
Calculate the average number of successful finds expected from a specific set of exploration targets.

## Benefits

- Accounts for spatial and geological correlations between prospects.
- Provides mathematical estimates for risked resource volumes.
- Quantifies cumulative discovery chances for entire portfolios.
- Tests model sensitivity against changing geological correlations.

## How It Works

Connect your AI client to the Vinkius hosted MCP to start running geological math.

1. Connect your preferred MCP-compatible client to Vinkius.
2. Provide your geological prospect data and correlation values to your AI agent.
3. Ask the agent to run specific probability or volume calculations.
4. Receive precise mathematical outputs directly in your chat interface.

## Frequently Asked Questions

**How does this MCP handle multiple prospects?**
It uses spatial and geological correlations to calculate the cumulative likelihood of discovery across the entire group rather than treating them as isolated events.

**Can I use this with Claude or Cursor?**
Yes, you can connect this MCP to any MCP-compatible client like Claude, Cursor, or Windsurf through the Vinkius platform.

**What is a risked resource?**
A risked resource is an estimate of a potential find where the total volume is multiplied by the probability of success.

**Does this tool help with sensitivity analysis?**
Yes, you can use the correlation sensitivity tool to see how changes in geological correlation affect your discovery probability.

**Do I need to host the MCP myself?**
No, Vinkius hosts and manages the MCP for you. You just connect your client and start using the tools.

**How does geological correlation affect my results?**
High correlation means successes are likely to cluster. The `calculate_discovery_probability` tool adjusts the cumulative probability downward as correlation increases, reflecting that successes are not independent events.

**What is a risked resource estimate?**
A risked resource estimate is a volume-based metric where the theoretical total resource is adjusted by the probability of success. You can calculate this using `calculate_risked_resource`.

**Can I analyze how sensitive my model is to correlation assumptions?**
Yes, you can use the `analyze_correlation_sensitivity` tool to see how the probability of at least one success shifts when the correlation coefficient is adjusted.