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Vinkius

Scale AI Connector for AI agents.

11 live capabilities

Orchestrate data labeling and RLHF pipelines from your chat interface.

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AI Agent

Why people use Scale AI

Scale AI for Automating RLHF Workflows

With this Connector, you just tell your agent what to do. It handles the project creation and batch submission for you, so you can move from idea to training data in a few prompts.

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

What Vinkius changes

You can manage your entire Scale AI data pipeline through a simple conversation.

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

One account · 5,900+ Connectors

  1. Real-world use case 01

    Image Segmentation Project

    An ML engineer needs to label 5,000 images.

  2. Real-world use case 02

    RLHF Tuning

    A researcher wants to update instructions for a human feedback loop.

  3. Real-world use case 03

    Video Annotation

    A developer needs to tag video clips.

Complete set · 11capabilities

The complete Scale AI capability set.

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

Capability set01 / 03

01—04

4 capabilities in this set.

Part of 11 available through Scale AI.

  1. 01 Capability

    Cancel task

    Stop a pending task immediately. This helps you stay on budget by preventing unnecessary costs for data that's no longer needed.

  2. 02 Capability

    Create batch

    Group your data into a new batch. This allows you to organize high-volume work and trigger the labeling process for many items at once.

  3. 03 Capability

    Create image annotation task

    Submit a specific task for image annotation. This lets you send individual images to your labeling queue for specific projects.

  4. 04 Capability

    Create named entity recognition task

    Create a task for identifying and extracting entities from text. Use this to pull specific names or locations from large datasets.

Capability set02 / 03

05—08

4 capabilities in this set.

Part of 11 available through Scale AI.

  1. 05 Capability

    Create project

    Set up a new Scale AI project with specific labeling configurations. This is the first step in starting a new data collection or RLHF run.

  2. 06 Capability

    Create segment annotation task

    Submit a task for semantic segmentation of images or other media. Use this for complex pixel-level labeling jobs.

  3. 07 Capability

    Create text collection task

    Create a task for gathering and labeling text data. This is useful for building large-scale text datasets for model training.

  4. 08 Capability

    Create video playback annotation task

    Submit a task for video annotation and playback. This lets you send video files for temporal or object-based labeling.

Capability set03 / 03

09—11

3 capabilities in this set.

Part of 11 available through Scale AI.

  1. 09 Capability

    Finalize batch

    Complete a batch to trigger the actual labeling process. Use this to move your organized data into the production labeling queue.

  2. 10 Capability

    Get task

    Retrieve the status and details of a specific annotation task. This helps you monitor progress and keep your project on schedule.

  3. 11 Capability

    Update project params

    Change the instructions or configuration for an existing project. This lets you refine labeling quality without restarting the entire project.

Set up in minutes

One URL. Then ask Scale AI to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Scale AI 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_lsC40U9ook5OZ8hrmwvYLqntMTpGHdSfQWvhNSRl/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 Scale AI, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Scale AI for the conversation.

Where the request belongs

Work Scale AI can move forward.

Built around the request

This is for the ML engineer who's tired of manual data management and the data ops manager who needs to oversee hundreds of labeling tasks without a headache.

01

ML Engineer

Submits edge cases for labeling directly from training scripts or analysis notebooks.

02

Data Operations Manager

Monitors batch progress and updates labeling instructions without leaving the chat.

03

AI Researcher

Quickly spins up RLHF projects to validate new datasets for model testing.

Build the capability set

Add more capabilities.

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

Browse Connectors
Roboflow logo
01 29 capabilities

Roboflow

Manage computer vision workflows. upload images, train models, and manage datasets directly from your AI agent.

View Connector
Nyckel ML logo
02 10 capabilities

Nyckel ML

Classify data and perform semantic search via Nyckel. track ML functions, samples, and labels directly from your AI agent.

View Connector
Braintrust logo
03 10 capabilities

Braintrust

Automate AI evaluations with Braintrust. organize projects, test model datasets, run benchmarks, and manage prompts via any AI agent.

View Connector
SigmaMind AI logo
04 10 capabilities

SigmaMind AI

Train custom computer vision models with your own images and deploy object detection and classification without ML expertise.

View Connector
Anyscale logo
05 7 capabilities

Anyscale

Orchestrate your Anyscale infrastructure. manage LLM queries, vectors, services, and cluster batch jobs directly from your AI agent.

View Connector
Metatext logo
06 10 capabilities

Metatext

No-code NLP and AI model management via Metatext. run inference and manage datasets.

View Connector

Bring your own AI

Change the model, client or framework. Keep Scale AI connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
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  • Zed
  • Continue
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Before you connect

Questions about Scale AI.

The practical details behind the request, access and result.

What can I do with the Scale AI MCP?

You can manage your entire data labeling pipeline through a conversation. This includes creating projects, organizing batches, and submitting various annotation tasks like image segmentation or video playback.

How does the Scale AI MCP help with RLHF?

It allows you to quickly spin up RLHF projects and update project parameters on the fly. This makes it much easier to refine instructions for human feedback loops without manual dashboard updates.

Can I use the Scale AI MCP for video labeling?

Yes, the Connector includes specific capabilities for video playback annotation. You can submit video files to the queue and have them processed for temporal or object-based labeling.

Does the Scale AI MCP support semantic segmentation?

Yes, it supports semantic segmentation tasks. You can submit these tasks directly through your AI agent to get pixel-level labeling for your training data.

How do I manage my budget with the Scale AI MCP?

You can monitor your spending by checking the status of individual tasks. If you need to stop work on a specific item, you can cancel pending tasks to avoid unnecessary costs.

Can I update project instructions with the Scale AI MCP?

Yes, you can update project parameters dynamically. If your labeling requirements change, just tell your agent to update the instructions for the active project.

How do I start a high-volume labeling job using batches?

First, use create_batch to initialize a group for your project. After submitting your tasks to this batch, call finalize_batch to signal Scale to begin the labeling process.

Can I check the status of a specific annotation task?

Yes, use the get_task capability with the specific Task ID. It will return the full metadata, current status, and any available results for that unit of work.

What should I do if I submitted a task by mistake?

You can use the cancel_task capability with the Task ID. If you need to reuse the unique identifier, you can also set the clear_unique_id parameter to true.

One connection away

Give your agent a direct line to Scale AI.

Connect Scale AI once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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