CursorIDE

Roboflow MCP Server

Bring Computer Vision
to Cursor

Name: Roboflow MCP Server
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

Learn how to connect Roboflow to Cursor and start using 29 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Add Projects To FolderAuto LabelCancel TrainingCreate Annotation JobCreate FolderCreate ProjectDelete ImagesDelete ProjectDownload DatasetFork Universe ProjectGet Async TaskGet Dataset HealthGet ImageGet ProjectGet RootGet Training ResultsGet VersionList FoldersList TrashList Workspace ProjectsManage Image TagsRestore TrashRun InferenceSearch Project ImagesSearch Workspace ImagesStart TrainingStop TrainingUpload AnnotationUpload Image

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the Roboflow MCP Server?

Connect Roboflow to your AI agent to streamline your computer vision pipeline. From dataset management to model training and inference, handle your entire CV lifecycle through natural language.

What you can do

Workspace & Project Management — List projects, create new ones, or fork from Roboflow Universe to jumpstart your development.
Dataset Operations — Upload images (via URL or Base64), manage versions, and download datasets in various formats like COCO or YOLO.
Model Training — Start training runs, monitor results, and retrieve precise performance metrics (mAP, precision, recall) for any version.
Image Search — Search and filter images within your workspace to audit your data and improve model accuracy.
Inference & Results — Run inference on images and retrieve results to verify model behavior in real-time.

How it works

Subscribe to this server
Enter your Roboflow Private API Key
Start building and managing vision models from Claude, Cursor, or any MCP-compatible client

Who is this for?

ML Engineers — monitor training progress and dataset health without leaving the terminal or IDE.
Data Scientists — quickly query dataset versions and export data for custom training scripts.
Product Teams — audit model performance and visualize inference results through simple conversation.

Built-in capabilities (29)

add_projects_to_folder

Add projects to a folder (Enterprise)

auto_label

Start an auto-labeling job using foundation models

cancel_training

Cancel an active training job

create_annotation_job

Assign a batch of images to a labeler and reviewer

create_folder

Create a project folder (Enterprise)

create_project

Create a new project in a workspace

delete_images

Delete multiple images from a project

delete_project

Delete a project or version (moves to Trash)

download_dataset

Retrieve a download link for a zipped dataset in a specific format

fork_universe_project

Fork a public project from Roboflow Universe

get_async_task

Track long-running operations like forking or large exports

get_dataset_health

Check dataset health (class distribution, missing annotations, etc)

get_image

Get details for a specific image

get_project

Get project details, metadata, and versions

get_root

Verify authentication and retrieve default workspace

get_training_results

Retrieve metrics and status for a version training run

get_version

Retrieve metadata for a specific dataset version

list_folders

List project folders in a workspace (Enterprise)

list_trash

List items in the workspace trash

list_workspace_projects

List information about a workspace and its projects

manage_image_tags

Add, remove, or set tags on an image

restore_trash

Restore an item from the trash

run_inference

Run inference on an image using hosted models

search_project_images

Search and filter images within a specific project

search_workspace_images

Search and filter images within a workspace

start_training

Start training a model on a dataset version

stop_training

Early stop an active training job

upload_annotation

Attach an annotation file to an existing image

upload_image

Upload an image to a project

Why Cursor?

Cursor's Agent mode turns Roboflow into an in-editor superpower. Ask Cursor to generate code using live data from Roboflow and it fetches, processes, and writes. all in a single agentic loop. 29 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.

—
Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context
—
Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards
—
MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment
—
VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools

See it in action

Roboflow in Cursor

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Roboflow and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Roboflow to Cursor through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	Raw MCP	Vinkius
Server catalog	Find and host yourself	4,000+ managed
Infrastructure	Self-hosted	Sandboxed V8 isolates
Credential handling	Plaintext in config	Vault + runtime injection
Data loss prevention	None	Configurable DLP policies
Kill switch	None	Global instant shutdown
Financial circuit breakers	None	Per-server limits + alerts
Audit trail	None	Ed25519 signed logs
SIEM log streaming	None	Splunk, Datadog, Webhook
Honeytokens	None	Canary alerts on leak
Custom domains	Not applicable	DNS challenge verified
GDPR compliance	Manual effort	Automated purge + export

Unlock for AI Agents View step-by-step setup guide for Cursor →

Enterprise Security

Why teams choose Vinkius for Roboflow in Cursor

The Roboflow MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 29 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in Cursor only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Unlock for AI Agents View full Roboflow details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

The Vinkius Advantage

How Vinkius secures
Roboflow for Cursor

Every tool call from Cursor to the Roboflow MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

How can I verify if my Roboflow API key is correctly configured?

You can use the get_root tool. It will attempt to authenticate with your key and return the default workspace details if successful.

Can I get the training performance metrics for a specific model version?

Yes! Use the get_training_results tool by providing the workspace, project, and version ID. It returns mAP, precision, recall, and other training metrics.

Is it possible to export my dataset to a specific format like YOLOv5?

Absolutely. Use the download_dataset tool and specify the format parameter (e.g., 'yolov5pytorch') to receive a download link for your zipped dataset.

What is Agent mode and why does it matter for MCP?

Agent mode is Cursor's autonomous execution mode where the AI can perform multi-step tasks: reading files, editing code, running terminal commands, and calling MCP tools. Without Agent mode, Cursor operates in a simpler ask-and-answer mode that doesn't support tool calling. Always ensure you're in Agent mode when working with MCP servers.

Where does Cursor store MCP configuration?

Cursor looks for MCP server configurations in a mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.

Can Cursor use MCP tools in inline edits?

No. MCP tools are only available in Agent mode through the chat panel. Inline completions and Tab suggestions do not trigger MCP tool calls. This is by design. tool calls require user visibility and approval.

How do I verify MCP tools are loaded?

Open Settings → Features → MCP and look for your server name. A green indicator means the server is connected. You can also check Agent mode's available tools by clicking the tools dropdown in the chat panel.