Cerbos (Access Control) MCP Server for Pydantic AIGive Pydantic AI instant access to 19 tools to Add Policy, Add Schema, Authzen Evaluation, and more

Name: Cerbos (Access Control)
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 (Access Control) 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 (Access Control) MCP Server for Pydantic AI is a standout in the Developer Tools category — giving your AI agent 19 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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+ other MCP clients

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 (Access Control) "
            "(19 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Cerbos (Access Control)?"
    )
    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 (Access Control) MCP Server

Connect your Cerbos instance to any AI agent to streamline authorization management and policy auditing through natural language.

Pydantic AI validates every Cerbos (Access Control) tool response against typed schemas, catching data inconsistencies at build time. Connect 19 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 Checks — Use check_resources to evaluate if a principal (user) has the rights to perform specific actions on resources.
Query Planning — Generate AST query plans with plan_resources to filter database results based on complex authorization logic.
Policy Management — List, retrieve, add, or delete policies (RBAC/ABAC) using the Admin API tools like list_policies and add_policy.
Schema & Auditing — Inspect resource schemas and review access logs with list_auditLogs to ensure compliance.
Health & Metrics — Monitor your PDP (Policy Decision Point) status with get_health and get_metrics directly from the chat.

The Cerbos (Access Control) MCP Server exposes 19 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 19 Cerbos (Access Control) tools available for Pydantic AI

When Pydantic AI connects to Cerbos (Access Control) 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.

add

Add policy on Cerbos (Access Control)

Add a new policy

add

Add schema on Cerbos (Access Control)

Add or update a schema

authzen

Authzen evaluation on Cerbos (Access Control)

Perform a single AuthZEN access evaluation

authzen

Authzen evaluations on Cerbos (Access Control)

Perform batch AuthZEN access evaluations

check

Check resources on Cerbos (Access Control)

Check permissions for a set of resources

delete

Delete policy on Cerbos (Access Control)

Delete a policy by ID

disable

Disable policy on Cerbos (Access Control)

Disable a policy

enable

Enable policy on Cerbos (Access Control)

Enable a policy

get

Get authzen config on Cerbos (Access Control)

Get AuthZEN configuration metadata

get

Get health on Cerbos (Access Control)

Get Cerbos health status

get

Get metrics on Cerbos (Access Control)

Get Prometheus metrics from Cerbos

get

Get policy on Cerbos (Access Control)

Get a specific policy by ID

get

Get schema on Cerbos (Access Control)

Get a specific schema by ID

get

Get server info on Cerbos (Access Control)

Get Cerbos server version and build information

list

List audit logs on Cerbos (Access Control)

List audit logs

list

List policies on Cerbos (Access Control)

List all policies

list

List schemas on Cerbos (Access Control)

List all schemas

plan

Plan resources on Cerbos (Access Control)

Produce a query plan (AST) for filtering resources

update

Update policy on Cerbos (Access Control)

Update an existing policy

Connect Cerbos (Access Control) to Pydantic AI via MCP

Follow these steps to wire Cerbos (Access Control) 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 19 tools from Cerbos (Access Control) with type-safe schemas

Why Use Pydantic AI with the Cerbos (Access Control) MCP Server

Pydantic AI provides unique advantages when paired with Cerbos (Access Control) 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 (Access Control) 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 (Access Control) connection logic from agent behavior for testable, maintainable code

Cerbos (Access Control) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Cerbos (Access Control) MCP Server delivers measurable value.

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

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

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

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

Example Prompts for Cerbos (Access Control) in Pydantic AI

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

"Check if user 'user_123' with role 'admin' can 'delete' the resource 'document:abc'."

"Show me the health status and version of my Cerbos server."

"List all policies and tell me if there are any for the 'expense' resource."

Troubleshooting Cerbos (Access Control) MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Cerbos (Access Control) + Pydantic AI FAQ

Common questions about integrating Cerbos (Access Control) 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 (Access Control) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

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