MCP For Claude Marketplace
Resource Model Validation

Resource Model Validation MCP Connector for Claude

A+

Validate mineral resource block models using statistical analysis and spatial swath plots.

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

This MCP server provides a specialized toolset for evaluating the accuracy and reliability of mineral resource block models. It allows for direct comparison between estimated model grades and measured sample data. Users can perform global statistical comparisons using get_statistical_summary, evaluate spatial accuracy through generate_swath_analysis, compute advanced error metrics with calculate_validation_metrics, and pinpoint geographic discrepancies using detect_local_bias.

resource-modelingblock-modelvalidationgeologystatistics

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

calculate_validation_metrics

Computes advanced error metrics to quantify model quality

detect_local_bias

Identifies specific geographic areas where the model is significantly overestimating or underestimating

generate_swath_analysis

Evaluates the spatial accuracy of the model by comparing averages along a chosen axis

get_statistical_summary

Provides a high-level comparison of the globalThis averages between the model and the samples

See how to talk to your AI agent using Resource Model Validation.

Compare the global averages of these sample grades [12.5, 13.2, 11.8] and model grades [12.8, 13.0, 12.1].

The model mean is 12.63 and the sample mean is 12.5. The mean difference is -0.13, indicating a slight overestimation by the model.

Calculate the validation metrics for sample grades [10.0, 15.0] and model grades [11.0, 14.0].

The RMSE is 1.0, the mean error is 0.0, and the relative error is 0.0.

Run a swath analysis along the Z axis with 5 slices using the provided points.

The swath analysis along the Z axis is complete. The maximum deviation across the 5 slices is 0.45 grade units.

You can use `calculate_validation_metrics` to compute RMSE and mean error, or `detect_local_bias` to find specific geographic zones where the model deviates from sample data.

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