Bring Data Labeling
to AutoGen
Learn how to connect Scale AI to AutoGen 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 AutoGen?
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Scale AI tools. Connect 11 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
- —
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Scale AI tools to solve complex tasks
- —
Role-based architecture lets you assign Scale AI tool access to specific agents. a data analyst queries while a reviewer validates
- —
Human-in-the-loop support: agents can pause for human approval before executing sensitive Scale AI tool calls
- —
Code execution sandbox: AutoGen agents can write and run code that processes Scale AI tool responses in an isolated environment
Scale AI in AutoGen
Scale AI and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Scale AI to AutoGen 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 AutoGen
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 AutoGen 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 AutoGen
Every tool call from AutoGen 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 AutoGen connect to MCP servers?
Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Scale AI tools during their conversation turns.
Can different agents have different MCP tool access?
Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.
Does AutoGen support human approval for tool calls?
Yes. Configure human-in-the-loop mode so agents pause and request approval before executing sensitive MCP tool calls.
McpWorkbench not found
Install: pip install "autogen-ext[mcp]"
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