How to Use the Cerbos (Access Control) MCP in LlamaIndex

Q: How does LlamaIndex index data from Cerbos (Access Control)?

It uses the MCP client to fetch raw data via tools like listpolicies and listschemas. It then parses these text definitions into nodes and inserts them into your local vector index.

Q: Can I build a security QA bot using Cerbos (Access Control) and LlamaIndex?

Yes. By feeding tools like getpolicy and listauditlogs into a FunctionAgent, your bot can answer complex questions about your authorization setup using real-time data.

Q: Does LlamaIndex support the AuthZEN standard in Cerbos (Access Control)?

Yes, through the authzenevaluation and authzenevaluations tools. Your agent can perform single or batch evaluations and index the results to find patterns in denied requests.

Q: How do I prevent hallucinations when querying my access rules?

The agent always fetches the live policy definition using getpolicy before answering. This guarantees the response is based on the actual active rules in your Cerbos instance.

Q: Is my Cerbos schema data safe?

Yes. The schemas and policies retrieved via getschema and listpolicies are processed locally within your secure Vinkius environment. This MCP Server processes your configurations without exposing them to external training sets.

Index your Cerbos access rules and audit logs into LlamaIndex to query permissions with zero hallucinations.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Cerbos (Access Control) MCP to LlamaIndex

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

GDPR Free for Subscribers

Setup Cerbos (Access Control) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index policies into your knowledge base

The `list_policies` tool pulls your active access control rules directly into LlamaIndex using the MCP protocol. The framework indexes these rules as documents, allowing your agent to run semantic searches over complex permission structures instead of guessing how they work. When you need to inspect a specific rule, the agent uses `get_policy` to fetch the raw YAML definition. This keeps your agent's context window clean by only pulling the exact policy blocks relevant to the user's query.

Semantic search over Cerbos audit logs

The `list_audit_logs` tool retrieves historical access decisions to build a searchable security index. LlamaIndex stores these logs in a vector database, letting you ask natural language questions about who accessed what resource and when. To resolve conflicts, the agent pairs log searches with `get_schema` to verify if the resource structure changed. This grounds your security audits in real system state rather than LLM assumptions.

Query planning with this LlamaIndex MCP Server

The `plan_resources` tool generates an abstract syntax tree (AST) representing the query plan for filtering resources. LlamaIndex uses this AST to structure database queries that match your Cerbos permissions precisely. This integration ensures that when your RAG application retrieves documents, it only fetches records the user is authorized to see. The agent verifies the plan using `get_authzen_config` to align with standard authorization metadata.

Setup guide

Set up Cerbos (Access Control) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Cerbos (Access Control) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Cerbos (Access Control) tools.",
)
response = await agent.run("List recent Cerbos (Access Control) data")

Get your connection token →

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cerbos (Access Control) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cerbos (Access Control) MCP in LlamaIndex

It uses the MCP client to fetch raw data via tools like `list_policies` and `list_schemas`. It then parses these text definitions into nodes and inserts them into your local vector index.

Yes. By feeding tools like `get_policy` and `list_audit_logs` into a FunctionAgent, your bot can answer complex questions about your authorization setup using real-time data.

Yes, through the `authzen_evaluation` and `authzen_evaluations` tools. Your agent can perform single or batch evaluations and index the results to find patterns in denied requests.

The agent always fetches the live policy definition using `get_policy` before answering. This guarantees the response is based on the actual active rules in your Cerbos instance.

Yes. The schemas and policies retrieved via `get_schema` and `list_policies` are processed locally within your secure Vinkius environment. This MCP Server processes your configurations without exposing them to external training sets.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript