How to Use the Cerbos MCP in LlamaIndex

Q: Is Cerbos data compatible with LlamaIndex tool filters?

Yes, you can use the allowedtools filter to restrict which Cerbos functions your agent can access. It ensures the model only uses the tools you want.

Index your authorization results into LlamaIndex for grounded, searchable access control knowledge.

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Connect Cerbos MCP to LlamaIndex

Create your Vinkius account to connect Cerbos 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.

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Setup Cerbos with LlamaIndex

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Grounding LlamaIndex with Cerbos data

Take the output from `check_resources` and pipe it into your vector store. Your agents now have a searchable index of what a principal can actually access. It stops your model from hallucinating permissions. When the agent asks what a user can see, it queries the index built from your live Cerbos data.

Automated permission indexing

Use the `authzen_evaluations` tool to pull bulk access data and feed it directly into your RAG pipeline. LlamaIndex treats these results as documents that the model can reference during a query. This keeps your agent's knowledge current with your actual policy state. You don't need to manually sync your security definitions with your index.

Querying Cerbos configurations in LlamaIndex

Run `get_server_info` and `get_authzen_config` to track versioning and policy endpoints within your knowledge base. It provides context for the agent when it needs to understand the infrastructure it's querying. Having the configuration indexed means the agent knows exactly which API version it's talking to. It makes the entire RAG application more aware of its own environment.

Setup guide

Set up Cerbos 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 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 tools.",
)
response = await agent.run("List recent Cerbos data")

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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 MCP in LlamaIndex

Use the McpToolSpec to wrap the server tools. You can then pass these to your agent to combine API data with your document search.

Absolutely, index the output of the evaluation tools into your vector store. This lets the agent perform semantic searches over permission sets.

Yes, you can use the allowed_tools filter to restrict which Cerbos functions your agent can access. It ensures the model only uses the tools you want.

It provides factual, real-time authorization state. Your agent relies on actual policy evaluation rather than guessing user access rights.

The server treats your policy definitions and evaluation requests as ephemeral. No user-identifiable information is persisted, and the connection is restricted to your specific endpoint token.

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