Cerbos MCP Server for LlamaIndexGive LlamaIndex 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

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cerbos as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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

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python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Cerbos. "
            "You have 6 tools available."
        ),
    )

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

asyncio.run(main())

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About Cerbos MCP Server

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

LlamaIndex agents combine Cerbos tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex

When LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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

Why Use LlamaIndex with the Cerbos MCP Server

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

Data-first architecture: LlamaIndex agents combine Cerbos tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Cerbos tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Cerbos, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Cerbos tools were called, what data was returned, and how it influenced the final answer

Cerbos + LlamaIndex Use Cases

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

Hybrid search: combine Cerbos real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Cerbos to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cerbos for fresh data

Analytical workflows: chain Cerbos queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Cerbos in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Cerbos + LlamaIndex FAQ

Common questions about integrating Cerbos MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Cerbos tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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