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Vinkius

DVC Connector for AI agents.

6 live capabilities

Track machine learning experiments and data versioning in real time.

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AI Agent

Why people use DVC

DVC for Machine Learning Experiment Tracking

With this Connector, you just ask your agent for the best performing model from last week. It pulls the data directly from DVC Studio, gives you the numbers you need, and lets you stay focused on the science rather than the admin work.

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

What Vinkius changes

You get a conversational interface for your entire DVC Studio experiment history.

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

One account · 5,900+ Connectors

  1. Real-world use case 01

    Comparing model accuracy

    A data scientist asks for the best accuracy from the last 5 runs to decide which model to deploy.

  2. Real-world use case 02

    Onboarding a new team member

    An ML engineer checks what permissions the new hire has to ensure they can access the right datasets.

  3. Real-world use case 03

    Auditing project history

    A team lead asks for a list of all projects to see what is currently in the production pipeline.

Complete set · 6capabilities

The complete DVC capability set.

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

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through DVC.

  1. 01 Capability

    List views

    Show all active dashboard layouts in your workspace. This helps you see how your data is structured.

  2. 02 Capability

    Get view

    Pull the configuration for a specific dashboard. Use this to see the exact layout of a view.

  3. 03 Capability

    List projects

    List every project in your organization. Use this to get a high-level overview of your work.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through DVC.

  1. 04 Capability

    Get project

    Fetch the metadata for a specific project. It gives you the details you need for a single repository.

  2. 05 Capability

    List experiments

    Show all model runs and experiments. This lets you see the history of your training cycles.

  3. 06 Capability

    Get user

    Retrieve the profile of a specific user. This is useful for checking identity and permissions.

Set up in minutes

One URL. Then ask DVC to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use DVC 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_Myft115Qe8hEZoVFuhiTYRKEW5euUlsOR2XqjK69/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 DVC, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable DVC for the conversation.

Where the request belongs

Work DVC can move forward.

Built around the request

For the ML engineer who is tired of digging through old logs to find out why a model failed. It's for anyone who needs to see the why behind the data without manual searching.

01

Data Scientist

Audits model runs to compare accuracy across different training epochs.

02

ML Engineer

Validates repository connections and checks permission scopes for team members.

03

Team Lead

Monitors the progress of multiple projects across the organization's workspace.

04

DevOps Engineer

Debugs DVC Studio integrations and verifies access tokens.

Build the capability set

Add more capabilities.

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

Browse Connectors
MLflow (ML Lifecycle Management) logo
01 6 capabilities

MLflow (ML Lifecycle Management)

Manage ML lifecycle via MLflow. track training runs, monitor metrics, and audit the model registry.

View Connector
Neptune.ai (ML Experiment Tracking) logo
02 6 capabilities

Neptune.ai (ML Experiment Tracking)

Manage ML experiments via Neptune.ai. track training runs, monitor metrics, and audit model versions.

View Connector
Comet ML logo
03 6 capabilities

Comet ML

Manage machine learning experiments via Comet. track model metrics, audit project workspaces, and inspect ML run parameters directly from any AI agent.

View Connector
Weights & Biases logo
04 6 capabilities

Weights & Biases

Track experiments, monitor ML runs, and manage artifacts on WandB. the developer platform for AI.

View Connector
Replicate logo
05 12 capabilities

Replicate

Equip your AI to dynamically search, run, and monitor thousands of open-source machine learning models hosted on Replicate via simple text commands.

View Connector
DataRobot logo
06 6 capabilities

DataRobot

Manage AutoML via DataRobot. monitor projects and models, track deployments, and audit ML datasets directly from any AI agent.

View Connector

Bring your own AI

Change the model, client or framework. Keep DVC 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 DVC.

The practical details behind the request, access and result.

How can I use DVC MCP to track my ML models?

You can use it to query your experiment history and metrics directly through your AI client. It connects to DVC Studio so you can ask questions about your past runs and model performance in natural language.

Can I see my experiment history with DVC MCP?

Yes, you can list all previous experiments and view specific metric arrays. This helps you compare different training cycles without leaving your chat interface.

Does DVC MCP work with my current DVC Studio account?

It does. You just need to provide your DVC Studio Client Access Token. Once connected, your AI agent can access your projects, views, and experiment data.

How do I check my project permissions using DVC MCP?

The Connector allows you to retrieve user profiles and check identity roles. You can ask your agent who has access to specific projects or what permissions a team member holds.

Can I get specific metrics from past runs?

Yes, you can retrieve complex metric arrays from specific experiment epochs. This is great for doing deep data analysis on your best performing models.

Is DVC MCP good for team leads?

It's excellent for team leads who need to monitor organization workspaces. You can quickly list all projects and check the progress of multiple experiments across the team.

Can my agent list all experiments for a specific DVC project?

Yes. Use the 'list_experiments' capability. Provide the project ID, and the agent will iterate through the model runs, returning a detailed history of metrics and execution logs for that project.

How do I see my custom dashboard views via chat?

Use the 'list_views' capability to see all your dashboard layouts. You can then use 'get_view' with a specific ID to retrieve the structural configuration and settings for that exact UI representation.

Can I audit my DVC Studio project settings through the agent?

Absolutely. The 'list_projects' and 'get_project' capabilities allow your agent to analyze identifier boundaries and repository metadata, helping you verify project connections and team mappings natively.

One connection away

Give your agent a direct line to DVC.

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

Explore every Connector No credit card required · Free tier available