Vinkius

Roboflow MCP. Manage the full CV lifecycle with natural prompts.

Roboflow manages your entire computer vision pipeline, letting you handle everything from dataset uploads to model training runs through natural language conversation. You can create new projects, download datasets in COCO or YOLO formats, and monitor metrics like mAP without leaving your agent.

Roboflow MCP is compatible with Claude Claude
Roboflow MCP is compatible with ChatGPT ChatGPT
Roboflow MCP is compatible with Cursor Cursor
Roboflow MCP is compatible with Gemini Gemini
Roboflow MCP is compatible with Windsurf Windsurf
Roboflow MCP is compatible with VS Code VS Code
Roboflow MCP is compatible with JetBrains JetBrains
Roboflow MCP is compatible with Vercel Vercel
See Vinkius in Action

Give Claude and any AI agent real-world access

Build and Organize Projects

Create new project folders or fork public Roboflow Universe projects into your private workspace.

Manage Image Assets and Data Versions

Upload images using URLs, manage dataset versions, check class distribution health, and download the data in required formats like COCO or YOLO.

Train and Monitor Models

Start model training runs on specific dataset versions and retrieve detailed performance metrics, including mAP, precision, and recall.

Search and Verify Data

Filter images within a project or an entire workspace to audit data quality or run real-time inference against hosted models.

Waiting for input…

AI Agent
Roboflow

What AI agents can do with Roboflow: 29 Tools for Computer Vision

These tools let you manage every step of a computer vision project, allowing your agent to organize projects, upload assets, train models, and verify data quality using natural language.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Roboflow MCP

Add Projects To Folder

Adds existing projects into a designated folder within an enterprise workspace.

Auto Label

Starts an automated labeling job using foundation models to speed up annotation.

Cancel Training

Stops a model training run that is currently active, saving computational resources.

Create Annotation Job

Assigns a batch of images to specific labelers and reviewers for human annotation...

Create Folder

Creates new project folders within an enterprise workspace structure.

Create Project

Sets up a brand new, empty machine learning project in your account.

Delete Images

Removes multiple images from a specific project folder.

Delete Project

Deletes an entire project or dataset version, moving it to the trash bin.

Download Dataset

Generates a download link for all images in a zipped file format (COCO, YOLO, etc.).

Fork Universe Project

Copies an existing public project from Roboflow Universe to your private account.

Get Async Task

Tracks the status of long-running background operations, like large exports or forks.

Get Dataset Health

Checks the structural integrity of a dataset, looking at class balance and missing annotations.

Get Image

Retrieves specific metadata details for one image file in your project.

Get Project

Fetches comprehensive details and version history for a given project.

Get Root

Verifies the connection credentials and retrieves the default workspace name.

Get Training Results

Pulls metrics and status updates for a specific, completed training run version.

Get Version

Gets metadata details about a particular version of your dataset.

List Folders

Lists all the project folders available within an enterprise workspace.

List Trash

Displays a list of items that have been deleted and moved to trash.

List Workspace Projects

Lists all projects associated with the current workspace account.

Manage Image Tags

Adds, removes, or sets descriptive tags on one or more images.

Restore Trash

Brings a deleted item back from the trash bin into active use.

Run Inference

Runs an immediate test on an image using pre-hosted models to see its predicted...

Search Project Images

Finds and filters images based on criteria within a single project.

Search Workspace Images

Searches for images across the entire workspace using defined filters.

Start Training

Initiates a model training job on a specified dataset version.

Stop Training

Stops an active model training process early if it's not performing well enough.

Upload Annotation

Attaches a file containing annotations to an existing image asset.

Upload Image

Uploads new images directly into a specified project folder.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Roboflow MCP is compatible with Claude

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The Roboflow integration is available immediately — no restart needed.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on each call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with Roboflow, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,200+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Connections are secured and governed automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog weekly
Roboflow MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Roboflow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on each call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

The headache of managing visual data assets.

Today, getting your model ready involves a mess of clicks. You upload raw images to one place, use another tool for annotation, and then you have to manually track which version of the dataset was used for training—all while jumping between five different browser tabs just to check if the job finished.

With this MCP, that process collapses into conversation. Your agent manages the entire visual data lifecycle. You tell it what to do—whether it's uploading a batch of images via URL or checking the class distribution using `get_dataset_health`. The result is immediate: you get clear status updates and actionable results right where you are.

Roboflow MCP delivers full dataset control.

You eliminate the need to manually manage versions or check data integrity. You no longer have to hunt for a specific annotated asset; your agent handles it using tools like `get_version` and `list_workspace_projects`. The complexity of CV pipelines is hidden behind simple commands.

The difference now is that you're not just running a tool; you're managing an entire, integrated pipeline. You gain control over every asset, every version, and the performance metrics in one single interaction.

What Roboflow MCP does for your AI

Managing a computer vision workflow used to mean jumping between six different dashboards: one for data annotation, another for version control, and a third just to check performance metrics. This MCP changes that. Your agent connects directly to Roboflow, letting you manage the full CV lifecycle using simple prompts. You can upload raw images via URL or Base64, track dataset health, and even fork public projects straight into your private workspace.

Whether you're an ML engineer monitoring a training run or a data scientist needing specific versions of data for custom scripts, this tool makes it happen in plain conversation. This capability is available through the Vinkius catalog, giving you one connection point to hundreds of other services. You start by setting up projects and then use your agent to run inference on images right away.

Built · Hosted · Managed by Vinkius Roboflow MCP - Manage Vision Data & Models
Server ID 019e38e5-62d8-7158-a3f9-2e42ac969dec
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Frequently asked questions about Roboflow MCP

How does Roboflow MCP handle large datasets? +

It handles them by providing tools like get_dataset_health to audit class distribution across massive uploads and allowing you to download structured data via download_dataset for external use.

Can I train a model using Roboflow MCP? +

Yes, your agent initiates training runs by calling start_training. It then lets you monitor the process and retrieve metrics using get_training_results until it's complete.

What if I need to improve my model with new images? +

You can use upload_image or search_workspace_images to find and add the necessary assets. After adding them, you run get_dataset_health again to ensure the balance is correct before retraining.

Does Roboflow MCP help with project organization? +

Absolutely. You can use create_project or add_projects_to_folder to structure your work, and even clone public examples using the fork_universe_project tool.

How do I test my model without running a full training job? +

You can run an immediate prediction check by calling run_inference. This tests your existing models on new images and gives you real-time results instantly.