Bring Data Labeling
to Windsurf
Learn how to connect Scale AI to Windsurf and start using 11 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Scale AI MCP Server?
Connect your Scale AI account to any AI agent to orchestrate large-scale data labeling and fine-tuning pipelines through natural conversation.
What you can do
- Project Management — Create and configure projects for specific labeling types like image annotation or semantic segmentation.
- Batch Operations — Organize high-volume work into batches and finalize them to trigger the labeling process.
- Multi-Modal Tasks — Submit tasks for Image Annotation, Semantic Segmentation, and Video Playback directly via API.
- Task Lifecycle — Retrieve detailed status of individual tasks or cancel pending ones to manage your budget and throughput.
- Parameter Tuning — Update project-level instructions and parameters dynamically to refine labeling quality.
How it works
- Subscribe to this server
- Enter your Scale AI Live API Key
- Start managing your data pipelines from Claude, Cursor, or any MCP-compatible client
Who is this for?
- ML Engineers — automate the submission of edge cases for labeling directly from training scripts or analysis notebooks.
- Data Operations Managers — monitor batch progress and update labeling instructions without leaving the chat interface.
- AI Researchers — quickly spin up RLHF or annotation projects to validate new model datasets.
Built-in capabilities (11)
Optionally clears the unique_id to reuse it. Cancel a pending task
Create a new batch
Create an Image Annotation task
Create a Named Entity Recognition task
Create a new Scale project
Create a Semantic Segmentation task
Create a Text Collection task
Create a Video Annotation task
Finalize a batch
Retrieve a specific task
Update project parameters
Why Windsurf?
Windsurf's Cascade agent chains multiple Scale AI tool calls autonomously. query data, analyze results, and generate code in a single agentic session. Paste Vinkius Edge URL, reload, and all 11 tools are immediately available. Real-time tool feedback appears inline, so you see API responses directly in your editor.
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Windsurf's Cascade agent autonomously chains multiple tool calls in sequence, solving complex multi-step tasks without manual intervention
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Purpose-built for agentic workflows. Cascade understands context across your entire codebase and integrates MCP tools natively
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JSON-based configuration means zero code changes: paste a URL, reload, and all 11 tools are immediately available
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Real-time tool feedback is displayed inline, so you see API responses directly in your editor without switching contexts
Scale AI in Windsurf
Scale AI and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Scale AI to Windsurf 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.
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 |
Why teams choose Vinkius for Scale AI in Windsurf
The Scale AI 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 11 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Windsurf 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.
* 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
How Vinkius secures
Scale AI for Windsurf
Every tool call from Windsurf to the Scale AI MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
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 tool 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 tool with the Task ID. If you need to reuse the unique identifier, you can also set the clear_unique_id parameter to true.
How does Windsurf discover MCP tools?
Windsurf reads the mcp_config.json file on startup and connects to each configured server via Streamable HTTP. Tools are listed in the MCP panel and available to Cascade automatically.
Can Cascade chain multiple MCP tool calls?
Yes. Cascade is an agentic system. it can plan and execute multi-step workflows, calling several tools in sequence to accomplish complex tasks without manual prompting between steps.
Does Windsurf support multiple MCP servers?
Yes. Add as many servers as needed in mcp_config.json. Each server's tools appear in the MCP panel and Cascade can use tools from different servers in a single flow.
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