What kind of data does this MCP need?

It requires grade data from your mineral resource block models and your measured sample data to perform comparisons.

Can I use this with Claude or Cursor?

Yes, you can connect this MCP to any MCP-compatible client like Claude, Cursor, or Windsurf.

How does it identify local bias?

The detect_local_bias capability looks for geographic areas where the model grade significantly deviates from the sample grade.

What is a swath analysis?

It is a method to check spatial accuracy by comparing average grades along a specific axis, such as the Z axis.

Do I need to host the MCP myself?

No, Vinkius hosts and manages the MCP for you, so it is ready to use as soon as you connect.

How can I check for systematic errors in my model?

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.

What is the purpose of swath analysis?

The generate_swath_analysis capability evaluates spatial accuracy by comparing average grades along a chosen axis (X, Y, or Z) within defined slices.

Does this capability handle sampling bias?

Yes, the get_statistical_summary capability includes an option to apply declustering weights to correct for sampling bias.

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Connect Resource Model Validation to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Verify block model accuracy against measured sample data.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through Resource Model Validation.

01
Calculate validation metrics
This capability computes error metrics to quantify how well your model matches the sample data. It provides the mathematical basis for assessing model quality.
02
Detect local bias
This capability identifies specific geographic areas where the model shows significant overestimation or underestimation. It helps you find localized errors in your resource estimation.
03
Generate swath analysis
This capability evaluates spatial accuracy by comparing averages along a chosen axis. It uses swath plots to check how the model behaves across different slices of the deposit.
04
Get statistical summary
This capability provides a high-level comparison of global averages between your model and your samples. It is the first step in checking for broad estimation errors.

Real work

What teams actually run

Model Quality Auditing

Run a full suite of error metrics to ensure a new block model meets required precision standards.

Spatial Trend Verification

Use swath analysis to check if the model correctly follows the grade trends along the X, Y, or Z axes.

Bias Troubleshooting

Locate specific areas in a deposit where the model is consistently overestimating grades.

Quick Statistical Checks

Compare global means between samples and models to catch major estimation errors immediately.

One connector, every AI

Resource Model Validation works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Resource Model Validation to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

881ms average. Fast in production.

Resource Model Validation is checked daily against the live service.

Daily averagePeak 1203ms

Oct 3Today

Fastest day: 735ms
Slowest day: 1203ms
14-day trend: Stable+2%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Resource Model Validation, so you can see the experience inside your AI.

It does not authenticate your account with Resource Model Validation. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Resource Model Validation Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_iZvzoUDDLKeVTunIMJpIhGxIDj9sJ3irnWTaI1wJ/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Resource Model Validation capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "resource-model-validation-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_iZvzoUDDLKeVTunIMJpIhGxIDj9sJ3irnWTaI1wJ/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Resource Model Validation in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Resource Model Validation owners hand off.

Geologists and resource engineers use this MCP to automate the tedious parts of model validation.

01
Resource Geologists
They hand off sample and model datasets to the AI to run quick validation checks.
02
Mining Engineers
They use the output to verify that resource estimates are reliable for mine planning.
03
Data Analysts
They use the statistical capabilities to quantify the precision of geological models.

FAQ

Questions Resource Model Validation owners ask.

01
What kind of data does this MCP need?
It requires grade data from your mineral resource block models and your measured sample data to perform comparisons.
02
Can I use this with Claude or Cursor?
Yes, you can connect this MCP to any MCP-compatible client like Claude, Cursor, or Windsurf.
03
How does it identify local bias?
The detect_local_bias capability looks for geographic areas where the model grade significantly deviates from the sample grade.
04
What is a swath analysis?
It is a method to check spatial accuracy by comparing average grades along a specific axis, such as the Z axis.
05
Do I need to host the MCP myself?
No, Vinkius hosts and manages the MCP for you, so it is ready to use as soon as you connect.
06
How can I check for systematic errors in my model?
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.
07
What is the purpose of swath analysis?
The generate_swath_analysis capability evaluates spatial accuracy by comparing average grades along a chosen axis (X, Y, or Z) within defined slices.
08
Does this capability handle sampling bias?
Yes, the get_statistical_summary capability includes an option to apply declustering weights to correct for sampling bias.

Explore

More in Data Analysis

Connect Resource Model Validation to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Calculate validation metrics

Detect local bias

Generate swath analysis

Get statistical summary

What teams actually run

Model Quality Auditing

Spatial Trend Verification

Bias Troubleshooting

Quick Statistical Checks

Building your own app? The connector is yours to use.

Resource Model Validation Connector

Claude Desktop

Resource Geologists

Mining Engineers

Data Analysts

What kind of data does this MCP need?

Can I use this with Claude or Cursor?

How does it identify local bias?

What is a swath analysis?

Do I need to host the MCP myself?

How can I check for systematic errors in my model?

What is the purpose of swath analysis?

Does this capability handle sampling bias?

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