How to Use the Cerbos (Access Control) MCP in OpenAI Agents SDK

Q: How do I connect Cerbos (Access Control) to the OpenAI Agents SDK?

Install the openai-agents package first. Create an MCPServerStreamableHttp instance with your Vinkius endpoint URL. Pass it into the mcpservers list when initializing your agent.

Q: Can the OpenAI Agents SDK cache tools from Cerbos (Access Control)?

Yes, you can set cacheToolsList=True in your MCP Server configuration. This prevents the agent from re-fetching the 19 available tools on every initialization, cutting down startup time significantly.

Q: Does the server support batch evaluations?

The server exposes the authzenevaluations tool specifically for this purpose. Your agent can validate multiple access requests in a single network call. This keeps latency low when checking permissions for large datasets.

Q: How do I debug policy errors with this MCP Server?

Use the getpolicy and getschema tools to inspect the current rules. The OpenAI dashboard traces these tool calls automatically. You can see exactly which policy version the agent retrieved during its reasoning loop.

Connect Cerbos (Access Control) to the OpenAI Agents SDK and manage authorization policies directly from your production Python agent.

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Connect Cerbos (Access Control) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Cerbos (Access Control) to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cerbos (Access Control) with OpenAI Agents SDK

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Evaluate AuthZEN permissions with OpenAI Agents SDK

Connecting the Cerbos (Access Control) MCP Server lets your OpenAI agent check permissions against your defined policies. By calling `authzen_evaluation` or `authzen_evaluations`, the system validates access rights before proceeding with any state-changing operations. Built-in guardrails in the SDK make this process incredibly safe. If a user asks the agent to perform an action they lack clearance for, the agent checks `check_resources` and immediately halts execution. You get full tracing in the OpenAI dashboard showing exactly why the authorization check failed.

Manage policy lifecycles autonomously

Cerbos (Access Control) handles authorization rules as code, and now your agent can update those rules dynamically. When business requirements shift, your specialized agent can execute `add_policy` or `update_policy` to modify access controls. It can also temporarily restrict access by calling `disable_policy` during an active incident. Handoffs between specialized agents keep your architecture clean. You might have one agent responsible for reading rules via `list_policies` and `get_policy`, which then hands off to an admin agent that actually applies changes. This ensures strict separation of duties within your agent system.

Audit access control decisions

Compliance requires strict visibility into authorization history, which the Cerbos (Access Control) MCP Server provides. Your AI client can pull historical data using the `list_audit_logs` tool to review past access decisions. This gives your production system the ability to generate compliance reports or investigate suspicious access patterns on demand. System health is equally critical for production deployments. The agent can monitor the underlying infrastructure by calling `get_health` and `get_metrics`. If the authorization service experiences latency, your agent knows immediately and can alert the engineering team.

Setup guide

Set up Cerbos (Access Control) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Cerbos (Access Control) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Cerbos (Access Control) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Cerbos (Access Control) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Cerbos (Access Control) Agent",
            instructions="You have access to Cerbos (Access Control) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cerbos. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cerbos (Access Control) MCP in OpenAI Agents SDK

Install the `openai-agents` package first. Create an `MCPServerStreamableHttp` instance with your Vinkius endpoint URL. Pass it into the `mcp_servers` list when initializing your agent.

Yes, you can set `cacheToolsList=True` in your MCP Server configuration. This prevents the agent from re-fetching the 19 available tools on every initialization, cutting down startup time significantly.

The server exposes the `authzen_evaluations` tool specifically for this purpose. Your agent can validate multiple access requests in a single network call. This keeps latency low when checking permissions for large datasets.

Use the `get_policy` and `get_schema` tools to inspect the current rules. The OpenAI dashboard traces these tool calls automatically. You can see exactly which policy version the agent retrieved during its reasoning loop.

The server only processes authorization policies, schemas, and resource attributes. It never accesses your underlying database records. Vinkius isolates this MCP connection in a V8 sandbox, ensuring your access control rules remain completely separated from other agent operations.

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