Bring Data Labeling
to CrewAI
Learn how to connect Scale AI to CrewAI 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 CrewAI?
When paired with CrewAI, Scale AI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Scale AI tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
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Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter and agents auto-discover every available tool at runtime - —
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
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Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Scale AI in CrewAI
Scale AI and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Scale AI to CrewAI 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 CrewAI
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 CrewAI 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 CrewAI
Every tool call from CrewAI 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 CrewAI discover and connect to MCP tools?
CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
Can different agents in the same crew use different MCP servers?
Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
What happens when an MCP tool call fails during a crew run?
CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
Can CrewAI agents call multiple MCP tools in parallel?
CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
Can I run CrewAI crews on a schedule (cron)?
Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.
MCP tools not discovered
Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
Agent not using tools
Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
Timeout errors
CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
Rate limiting or 429 errors
Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.
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