Bring Data Labeling
to Pydantic AI
Learn how to connect Scale AI to Pydantic AI 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 Pydantic AI?
Pydantic AI validates every Scale AI tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
- —
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Scale AI integration code
- —
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
- —
Dependency injection system cleanly separates your Scale AI connection logic from agent behavior for testable, maintainable code
Scale AI in Pydantic AI
Scale AI and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Scale AI to Pydantic AI 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 Pydantic AI
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 Pydantic AI 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 Pydantic AI
Every tool call from Pydantic AI 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 Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Scale AI MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
Explore More MCP Servers
View all →
Text Diff Engine
1 toolsExact character-by-character string comparison. Stop relying on LLM summaries and get absolute text diffs for code and contracts.
Zoho CRM Sales
10 toolsDeep deal management with quotes, products, sales orders, and invoices — complete sales cycle in Zoho CRM.
Moody's
8 toolsCredit ratings and risk analysis — access issuer ratings, issue details, and rating actions via Moody's.
Semgrep
10 toolsEquip your AI agent with read/write access to Semgrep's SAST platform to audit code security findings, update triage statuses, and enforce custom semantic rules.