MCP For Claude Marketplace
Kriging Estimation Model

Kriging Estimation Model MCP Connector for Claude

A+

Perform Ordinary Kriging to estimate block grades and spatial uncertainty.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides geostatistical tools for resource modeling using Ordinary Kriging. It allows AI agents to calculate block grades, estimation variances, and kriging weights from spatial sample data. Users can validate variogram parameters with validate_variogram_parameters, analyze sample density via get_spatial_correlation_stats, and retrieve specific sample influences using get_kriging_weights. The engine supports anisotropy and search ellipse constraints for precise spatial interpolation.

kriginginterpolationspatial-analysismininggeology

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

get_block_estimates

Calculates the estimated grade and uncertainty for a specific set of block discretization points

get_kriging_weights

Retrieves the specific influence (weights) each sample has on a target estimation point

get_spatial_correlation_stats

Provides high-level summary statistics regarding the density and distribution of samples within the search space

validate_variogram_parameters

Ensures the variogram model provided is physically and mathematically sound for kriging

See how to talk to your AI agent using Kriging Estimation Model.

Calculate the estimated grade for these block points using the provided sample data and variogram.

The estimated grade for the target block is 4.52 g/t with an estimation variance of 0.12.

What are the weights for the samples near the center point (10, 10, 10)?

The sample at (10, 10, 10) has a weight of 0.65, and the sample at (12, 10, 10) has a weight of 0.35.

Check the spatial correlation statistics for my sample data within a 50m radius.

Within the 50m search radius, there are 12 samples with an average distance to target of 24.5m and a coverage density of 0.08.

Ordinary Kriging is a geostatistical interpolation method that estimates values at unsampled locations by weighting nearby known samples based on spatial correlation.

Related Connectors

Reservoir Prediction Uncertainty

Reservoir Prediction Uncertainty MCP

4 tools Official

Quantifies uncertainty in reservoir predictions using parameter ranges and correlations.

A+ View details →
Grade Control Modeling

Grade Control Modeling MCP

4 tools Official

Models grade control for ore/waste discrimination to optimize extraction accuracy.

A+ View details →
Blast Vibration Predictor

Blast Vibration Predictor MCP

4 tools Official

Predict ground vibration intensity and regulatory compliance from blasting events.

A+ View details →
Resource Assessment Uncertainty

Resource Assessment Uncertainty MCP

4 tools Official

Quantify geological uncertainty using probabilistic Monte Carlo simulations.

A+ View details →