Cerbos MCP Server for Pydantic AIGive Pydantic AI instant access to 6 tools to Authzen Evaluation, Authzen Evaluations, Check Resources, and more

Name: Cerbos
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Cerbos through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Cerbos MCP Server for Pydantic AI is a standout in the Developer Tools category — giving your AI agent 6 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Cerbos "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Cerbos?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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

About Cerbos MCP Server

Connect your Cerbos instance to any AI agent to manage complex authorization policies through natural language conversation.

Pydantic AI validates every Cerbos tool response against typed schemas, catching data inconsistencies at build time. Connect 6 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.

What you can do

Permission Evaluation — Use check_resources to instantly verify if a principal can perform specific actions on resources based on your policies.
Query Planning — Generate AST-based query plans with plan_resources to filter database results according to user permissions.
AuthZEN Compliance — Leverage standardized access requests using authzen_evaluation and authzen_evaluations tools.
System Monitoring — Check instance health and build metadata using get_server_info and get_authzen_config.

The Cerbos MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 6 Cerbos tools available for Pydantic AI

When Pydantic AI connects to Cerbos through Vinkius, your AI agent gets direct access to every tool listed below — spanning authorization, rbac, abac, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

authzen

Authzen evaluation on Cerbos

Single action evaluation using the AuthZEN entity model

authzen

Authzen evaluations on Cerbos

Supports execute_all, deny_on_first_deny, and permit_on_first_permit semantics. Batch evaluation of multiple access requests using AuthZEN

check

Check resources on Cerbos

This is a read-only evaluation. Evaluates permissions for a principal on a set of resources

get

Get authzen config on Cerbos

Returns endpoint URLs for the AuthZEN APIs

get

Get server info on Cerbos

Returns the version and build details of the Cerbos instance

plan

Plan resources on Cerbos

Produces a query plan for obtaining a list of resources a principal is allowed to access

Connect Cerbos to Pydantic AI via MCP

Follow these steps to wire Cerbos into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Cerbos with type-safe schemas

Why Use Pydantic AI with the Cerbos MCP Server

Pydantic AI provides unique advantages when paired with Cerbos through the Model Context Protocol.

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 Cerbos integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Cerbos connection logic from agent behavior for testable, maintainable code

Cerbos + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Cerbos MCP Server delivers measurable value.

Type-safe data pipelines: query Cerbos with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Cerbos tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Cerbos and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Cerbos responses and write comprehensive agent tests

Example Prompts for Cerbos in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Cerbos immediately.

"Check if principal 'user_123' with role 'editor' can 'edit' resource 'document:abc'."

"Generate a query plan for 'view' action on 'expense' resources for principal 'manager'."

"Show me the Cerbos server build details."

Troubleshooting Cerbos MCP Server with Pydantic AI

Common issues when connecting Cerbos to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Cerbos + Pydantic AI FAQ

Common questions about integrating Cerbos MCP Server with Pydantic AI.

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 Cerbos MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.