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Vinkius

simple-statistics Connector for AI agents.

1 live capability

Clean your machine learning datasets by filling missing values with exact statistical measures.

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Why people use simple-statistics

Missing Value Imputer for ML Data Cleaning

This Connector changes that. You just tell your AI client to fill the gaps using the mean strategy. The Connector handles the math locally, swaps out the NaNs, and hands you back a clean dataset in seconds. You stop worrying about the manual labor and start focusing on the results.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get mathematically accurate data cleaning without using up your AI's context window.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 6,100+ Connectors

  1. Real-world use case 01

    Fixing Age columns in large datasets

    An ML engineer has a 10,000-row JSON file with missing ages.

  2. Real-world use case 02

    Standardizing discount fields

    A data analyst needs to ensure all missing discount entries in a sales report are set to zero to preserve business logic.

  3. Real-world use case 03

    Handling salary outliers

    A researcher uses the mean strategy to fill in missing salary data points in a survey, ensuring the distribution remains consistent.

Complete set · 1capability

The complete simple-statistics capability set.

These are the exact actions your AI can choose when you ask it to work with simple-statistics.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through simple-statistics.

  1. 01 Capability

    Impute missing values

    Fills NaN or missing values in a dataset using Mean, Median, Mode, or Zero. This ensures your data stays consistent for model training.

Set up in minutes

One URL. Then ask simple-statistics to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use simple-statistics from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_BTxif6Mgt22Ph4cDxZc0GCJG4IrQppXbcgk0qLS1/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it simple-statistics, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable simple-statistics for the conversation.

Where the request belongs

Work simple-statistics can move forward.

Built around the request

This is for data scientists and ML engineers who deal with large, messy datasets and need to clean them for model training without manual copy-pasting.

01

Machine Learning Engineer

Prepares training sets by cleaning thousands of rows of NaN values before feeding them into a model.

02

Data Scientist

Uses median and mean strategies to handle missing entries in research datasets to ensure statistical validity.

03

Data Analyst

Cleans up messy CSV exports by filling missing discount or price fields with zeros or modes.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Feature Scaler Engine logo
01 1 capability

Feature Scaler Engine

Standardize (Z-Score) or MinMax scale numeric columns with mathematical perfection local. Essential normalization for neural networks and clustering algorithms.

View Connector
Outlier Detection Engine logo
02 1 capability

Outlier Detection Engine

Identify statistical anomalies in massive datasets local using deterministic Z-Score and IQR methods. Stop LLMs from guessing which rows are outliers.

View Connector
SMOTE Oversampling Engine logo
03 1 capability

SMOTE Oversampling Engine

Balance skewed datasets instantly by generating mathematically sound synthetic minority data points via KNN.

View Connector
Statistics Engine logo
04 5 capabilities

Statistics Engine

A zero-latency statistical engine to process datasets. Instantly compute the exact mean, median, mode, standard deviation, and percentiles completely local.

View Connector
One-Hot Encoder Engine logo
05 1 capability

One-Hot Encoder Engine

Deterministically convert categorical text columns into dummy binary variables local. Essential machine learning data prep without LLM data corruption.

View Connector
PCA Dimensionality Engine logo
06 1 capability

PCA Dimensionality Engine

Perform native Principal Component Analysis to safely reduce high-dimensional datasets without losing critical variance.

View Connector

Bring your own AI

Change the model, client or framework. Keep simple-statistics connected.

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  • ChatGPT
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Before you connect

Questions about simple-statistics.

The practical details behind the request, access and result.

How does the Missing Value Imputer MCP handle large datasets?

It processes your data locally using a statistical engine. This means it can handle thousands of rows in milliseconds without hitting AI context limits.

Can I use the Missing Value Imputer MCP to fill data with specific math?

Yes, you can choose between Mean, Median, Mode, and Zero strategies depending on what fits your data best.

Does the Missing Value Imputer MCP keep my data private?

Yes, the mathematical calculations happen locally on your machine. Your raw data isn't sent to a third-party calculation service.

How do I choose between mean and median in the Missing Value Imputer MCP?

Use Mean if you want the mathematical average, or Median if your data has outliers that might skew a simple average.

Can the Missing Value Imputer MCP handle JSON files?

Yes, it works with your data regardless of format, as long as your AI client can pass the data to the Connector.

What happens to my data when I use the Missing Value Imputer MCP?

The Connector identifies the missing values and replaces them with the calculated statistic. It also provides a report on exactly what it did.

Does it modify the original data file on disk?

No. The engine processes the JSON payload entirely in memory and returns the cleaned array back to the AI. Your original files are never touched.

What happens if the entire target column is empty?

If you try to compute mean or median on a completely empty column, the engine throws a deterministic error explaining the issue. You can fall back to the 'zero' strategy instead.

How does it decide which cells are 'missing'?

The engine treats null, undefined, empty strings, and NaN as missing values. Any cell that cannot be parsed as a valid number is flagged for imputation.

One connection away

Give your agent a direct line to simple-statistics.

Connect simple-statistics once. Keep it beside 6,100+ managed Connectors when the next task needs more.

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