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Vinkius

array-ops Connector for AI agents.

3 live capabilities

Perform precise batching and deduplication on large datasets without context limits.

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AI Agent

Why people use array-ops

Deterministic Array Operations for High-Precision Data Engineering

This Connector changes that by moving the heavy lifting away from the AI's brain. When you ask for a transformation, the capability handles the math using a local engine. You get a perfectly formatted result every time, no matter how big the original list was.

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

What Vinkius changes

You get mathematically perfect data transformations 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

    Batching API requests

    A developer has 500 items to send to a rate-limited service.

  2. Real-world use case 02

    Cleaning CRM exports

    A user uploads a messy list of customers.

  3. Real-world use case 03

    Finding common leads

    A marketing lead has two different lists of emails.

Complete set · 3capabilities

The complete array-ops capability set.

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

Capability set01 / 01

01—03

3 capabilities in this set.

Part of 3 available through array-ops.

  1. 01 Capability

    Array chunk

    Splits a JSON array into smaller pieces of a specific size so you don't hit context limits.

  2. 02 Capability

    Array deduplicate

    Removes duplicate items from a list or filters objects based on a specific unique key.

  3. 03 Capability

    Array intersect

    Identifies every common item shared between two different JSON arrays instantly.

Set up in minutes

One URL. Then ask array-ops to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use array-ops 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_qEnuTZS4fCi8KZGbazejv5BrYjmtYtkaYZAYW7vG/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 array-ops, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable array-ops for the conversation.

Where the request belongs

Work array-ops can move forward.

Built around the request

This is for the data engineer who is tired of their AI forgetting items in a list of 1,000 objects or the backend dev who needs to batch API requests without manual scripting.

01

Data Engineer

Processing messy JSON exports from a database without losing records.

02

Backend Developer

Splitting large payloads into chunks to avoid hitting rate limits on external APIs.

03

Data Analyst

Quickly finding commonalities between two different customer lists.

Build the capability set

Add more capabilities.

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

Browse Connectors
Data Sorting & Filtering Engine logo
01 2 capabilities

Data Sorting & Filtering Engine

Perfect array manipulation. Sort and deduplicate massive JSON lists with native Vinkius Edge JavaScript instead of relying on token-limited LLMs.

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Object Hash Engine logo
02 1 capability

Object Hash Engine

Generate deterministic SHA-256 fingerprints of any JSON object. Keys are automatically sorted so {b:2,a:1} and {a:1,b:2} always produce the exact same hash. Essential for deduplication.

View Connector
Outlier Detection Engine logo
03 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
DataFrame Aggregator Engine logo
04 1 capability

DataFrame Aggregator Engine

Perform blazingly fast GroupBy and Aggregations on massive CSVs local. Save millions of AI tokens and get mathematically perfect sums, means, and counts.

View Connector
Statistics Engine logo
05 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
Fuzzy Match Search logo
06 1 capability

Fuzzy Match Search

Perform lightning-fast fuzzy string matching across large datasets. Find the closest matches instantly using Levenshtein distance.

View Connector

Bring your own AI

Change the model, client or framework. Keep array-ops 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 array-ops.

The practical details behind the request, access and result.

Does Deterministic Array Operations work with large JSON files?

Yes, it's designed specifically for large datasets that would normally overwhelm an AI's memory or context window.

Is my data safe when using Deterministic Array Operations?

Your data stays completely local. The Connector runs on your machine, so your files never leave your secure infrastructure.

How does Deterministic Array Operations handle duplicate objects?

You can tell the capability which specific key to look at, like an ID or an email, to ensure only unique records remain.

Can I use Deterministic Array Operations to avoid API rate limits?

Yes, the chunking capability is perfect for breaking down large payloads into smaller pieces that comply with external API limits.

Why should I use this instead of just asking my AI to do it?

AI models are probabilistic and often skip items in long lists. This capability uses a Javascript engine to guarantee 100% accuracy.

What kind of data can I process with Deterministic Array Operations?

It works with any JSON array, whether it's a list of strings, numbers, or complex objects.

Why use an Connector for Array Chunking?

AI models process text sequentially and struggle with counting large sequences. If you ask an AI to chunk an array of 50 items into groups of 7, it will likely miscount or hallucinate records. A deterministic Javascript capability guarantees zero data loss.

Can it deduplicate objects, not just strings?

Yes! The deduplicate_array capability performs deep stringification for objects. If you want to deduplicate by a specific property, just pass the key parameter (e.g., id or email), and it will filter unique records based on that key.

Are my data payloads sent externally during intersection?

No. The entire engine executes natively within your local V8 environment. Zero API requests are made, ensuring strict security compliance.

Why use an MCP for Array Chunking?

AI models process text sequentially and struggle with counting large sequences. If you ask an AI to chunk an array of 50 items into groups of 7, it will likely miscount or hallucinate records. A deterministic Javascript capability guarantees zero data loss.

One connection away

Give your agent a direct line to array-ops.

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

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