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Vinkius

ml-pca Connector for AI agents.

1 live capability

Reduce high-dimensional datasets into 2D or 3D components without losing variance.

Live agent request ml-pca / Connector

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AI Agent

Why people use ml-pca

PCA Dimensionality Engine for Precise Matrix Math

This Connector takes that burden off your agent. Instead of hoping the AI gets the math right, you let it call the PCA Dimensionality Engine. It handles the matrix transformations natively, giving you a mathematically perfect reduction of your data into 2D or 3D components.

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

What Vinkius changes

You get mathematically accurate data compression that an LLM simply can't do on its own.

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

One account · 5,900+ Connectors

  1. Real-world use case 01

    Customer Behavior Mapping

    A marketing analyst has 500 features on user clicks.

  2. Real-world use case 02

    Financial Correlation Analysis

    A quant wants to see the 5 biggest drivers in a 100-column correlation matrix.

  3. Real-world use case 03

    IoT Sensor Compression

    An IoT engineer has thousands of sensor inputs from a factory floor.

Complete set · 1capability

The complete ml-pca capability set.

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

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through ml-pca.

  1. 01 Capability

    Calculate pca

    Runs a PCA calculation on a dataset to reduce its dimensions. It returns the new components and the variance retained.

Set up in minutes

One URL. Then ask ml-pca to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use ml-pca 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_CTLJGZMOzWS3P0dPZpSvR6monLs0Y9Vip0jwJ7gT/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 ml-pca, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable ml-pca for the conversation.

Where the request belongs

Work ml-pca can move forward.

Built around the request

This is for the data scientist or ML engineer who is tired of their AI hallucinating during feature engineering. It's for anyone who needs to turn "too much data" into "usable insights" without losing the core signal.

01

Data Scientist

Uses it on Tuesday afternoons to clean up messy user behavior logs before building a visualization.

02

ML Engineer

Uses it to reduce feature sets for model training to prevent overfitting.

03

Research Analyst

Uses it to find the top 5 drivers in a 100-column financial correlation matrix.

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
K-Means Cluster Engine logo
02 1 capability

K-Means Cluster Engine

Group complex data points into optimal clusters with deterministic, high-speed Euclidean K-Means classification.

View Connector
Silhouette Score Engine logo
03 1 capability

Silhouette Score Engine

Evaluate the quality and cohesion of clustering algorithms (like K-Means) with mathematically perfect Silhouette scores computed local.

View Connector
Outlier Detection Engine logo
04 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
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
SMOTE Oversampling Engine logo
06 1 capability

SMOTE Oversampling Engine

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

View Connector

Bring your own AI

Change the model, client or framework. Keep ml-pca connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about ml-pca.

The practical details behind the request, access and result.

Can the PCA Dimensionality Engine handle very large datasets?

Yes, it's designed to handle thousands of features. It compresses the data into a few components so your AI agent can process it without hitting context limits.

Will I lose important information when I reduce my data?

Not if you check the variance. The Connector tells you exactly how much information is kept, so you can decide if the 2D or 3D output is sufficient for your needs.

How is this different from just asking my AI to summarize my data?

AI models often hallucinate when doing complex math. This Connector performs the math natively, ensuring the results are mathematically perfect every time.

What kind of data works best with the PCA Dimensionality Engine?

It works great for any high-dimensional numerical data, like customer behavior logs, financial records, or sensor readings that have many different variables.

Can I use this for 3D data visualization?

Exactly. It's perfect for turning complex data into 3D components, making it much easier for your agent to help you build maps or charts.

Is this capability good for identifying the main drivers in my data?

Yes, it identifies the primary factors that contribute most to the variance in your dataset, effectively stripping away the noise.

Does it guarantee exact mathematical precision?

Absolutely. It utilizes native V8 singular value decomposition algorithms to compute eigenvectors without any probabilistic hallucination.

How does it handle explained variance?

The engine automatically returns an array detailing the exact percentage of total dataset variance preserved by each calculated component.

Can it process large embedding vectors?

Yes, it is highly optimized to instantly compress complex, multi-dimensional embedding matrices generated by modern AI models.

One connection away

Give your agent a direct line to ml-pca.

Connect ml-pca once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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