Bring Bpm
to Pydantic AI
Learn how to connect Zeev 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 Zeev MCP Server?
What you can do
- List and manage your pending tasks in real-time.
- Start new process requests with custom form data.
- Complete tasks and make decisions directly from your AI agent.
- Delegate tasks to other team members and track process history.
How it works
- Get your Zeev domain and API Key from your profile settings.
- Enter your credentials in Vinkius platform.
- Start chatting with your Zeev agent to manage your workflows.
Who is it for?
- Process managers looking for automated workflow control.
- Operations teams needing quick task execution.
- Developers integrating BPM into their AI-driven applications.
Built-in capabilities (11)
Cancel an active process request
Start a new process request in Zeev
Delegate a task to another user
Finish/Complete a Zeev task
Get current user information
Get details of a process definition
Get details of a specific process request
Get details of a specific Zeev task
List available process definitions
List process requests (instances) in Zeev
List pending tasks in Zeev
Why Pydantic AI?
Pydantic AI validates every Zeev 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 Zeev integration code
- —
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Zeev connection logic from agent behavior for testable, maintainable code
Zeev in Pydantic AI
Zeev and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Zeev 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 Zeev in Pydantic AI
The Zeev 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
Zeev for Pydantic AI
Every tool call from Pydantic AI to the Zeev MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I start any process using the AI agent?
Yes, as long as you have the process ID and the required initial form data.
Is it possible to delegate tasks to specific users?
Yes, you can use the delegate_task tool by providing the Task ID and the target User ID.
Can I see the history of a request?
Yes, the get_request tool provides details including the current status and history of the instance.
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 Zeev MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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