Does this estimate cover labeling costs?

Yes. The capabilities factor in labeling needs and complexity. The budget estimate accounts for both automated processing and the cost associated with manual labeling efforts.

What variables do I need to provide?

You must provide details on the data volume, the required cleaning intensity, the number of labeling samples, and the overall preprocessing complexity.

Is this just a rough guess?

No. The MCP uses established models to calculate the investment. It provides measurable estimates for budget, timeline, and quality score, giving you concrete planning data.

Can I get a summary or separate estimates?

You can use the get_prep_summary capability for a full overview, or you can use specific capabilities like estimate_preparation_budget or estimate_preparation_timeline if you only need one metric.

How do I get a full overview of my data prep plan?

You can use the get_prep_summary capability to receive a unified report containing total cost, total weeks, quality score, and cost per GB.

Can I estimate the time required for manual labeling?

Yes, use estimate_preparation_timeline and specify the processingMode as 'manual_heavy' to account for human-in-the-loop requirements.

How is the data quality score determined?

The predict_data_quality_score capability calculates the score based on cleaning intensity, labeling volume, and the complexity of the data.

An astronaut overseeing a futuristic data hub

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Connect AI Data Preparation Estimator 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. Know the true cost and timeline before you write a line of code.

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 AI Data Prep Estimator.

01
Estimate preparation timeline
Answers "How many weeks will the data preparation phase take?"
02
Get prep summary
Answers "Give me a complete overview of the investment, time, and quality for my plan."
03
Predict data quality score
Answers "What will the quality of the prepared data be based on the chosen strategy?"
04
Estimate preparation budget
Answers "How much money will it cost to prepare this dataset?"

Real work

What teams actually run

Starting a new image recognition project

You need to know if your current dataset size and required labeling effort will fit within the quarter's budget and timeline.

Evaluating legacy data sources

Before committing to migrating old data, use the MCP to estimate the cost and time needed to clean and standardize the records.

Pitching an AI product to investors

Generate a detailed, data-backed investment summary that shows exactly what the data preparation phase requires.

Scaling a successful pilot project

Determine if the current data preparation process can handle a 10x increase in data volume without ballooning costs or delays.

One connector, every AI

AI Data Prep Estimator 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 AI Data Prep Estimator 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

931ms average. Fast in production.

AI Data Prep Estimator is checked daily against the live service.

Daily averagePeak 1141ms

Oct 3Today

Fastest day: 681ms
Slowest day: 1141ms
14-day trend: Slowing+28%

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 AI Data Prep Estimator, so you can see the experience inside your AI.

It does not authenticate your account with AI Data Prep Estimator. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

AI Data Prep Estimator Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_kwWZZlX9cEbWk5rrXHbqISdKwxwjnWrlaO34C5Bd/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 — AI Data Prep Estimator capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-data-preparation-estimator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_kwWZZlX9cEbWk5rrXHbqISdKwxwjnWrlaO34C5Bd/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 AI Data Prep Estimator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Data Prep Estimator owners hand off.

This MCP is essential for anyone leading a machine learning or data science initiative. If you're responsible for the project scope, budget, or timeline, you need this. It stops the common pitfall of starting a project without knowing the true data preparation cost.

01
Data Scientist
Use this to validate data assumptions and ensure the chosen preparation method is feasible before writing code.
02
Analytics Manager
Run this to build a solid business case, providing concrete cost and timeline estimates to executive leadership.
03
ML Engineer
Check the projected data quality score to validate the robustness of your data pipeline design.

FAQ

Questions AI Data Prep Estimator owners ask.

01
Does this estimate cover labeling costs?
Yes. The capabilities factor in labeling needs and complexity. The budget estimate accounts for both automated processing and the cost associated with manual labeling efforts.
02
What variables do I need to provide?
You must provide details on the data volume, the required cleaning intensity, the number of labeling samples, and the overall preprocessing complexity.
03
Is this just a rough guess?
No. The MCP uses established models to calculate the investment. It provides measurable estimates for budget, timeline, and quality score, giving you concrete planning data.
04
Can I get a summary or separate estimates?
You can use the get_prep_summary capability for a full overview, or you can use specific capabilities like estimate_preparation_budget or estimate_preparation_timeline if you only need one metric.
05
How do I get a full overview of my data prep plan?
You can use the get_prep_summary capability to receive a unified report containing total cost, total weeks, quality score, and cost per GB.
06
Can I estimate the time required for manual labeling?
Yes, use estimate_preparation_timeline and specify the processingMode as 'manual_heavy' to account for human-in-the-loop requirements.
07
How is the data quality score determined?
The predict_data_quality_score capability calculates the score based on cleaning intensity, labeling volume, and the complexity of the data.

Explore

More in Analytics

Connect AI Data Prep Estimator 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

Estimate preparation timeline

Get prep summary

Predict data quality score

Estimate preparation budget

What teams actually run

Starting a new image recognition project

Evaluating legacy data sources

Pitching an AI product to investors

Scaling a successful pilot project

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

AI Data Prep Estimator Connector

Claude Desktop

Data Scientist

Analytics Manager

ML Engineer

Does this estimate cover labeling costs?

What variables do I need to provide?

Is this just a rough guess?

Can I get a summary or separate estimates?

How do I get a full overview of my data prep plan?

Can I estimate the time required for manual labeling?

How is the data quality score determined?

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