How to Use the Drata MCP in LlamaIndex

Q: How do I convert Drata MCP tools into LlamaIndex tools?

Use the BasicMCPClient to connect to the Vinkius endpoint, then wrap it with McpToolSpec. Call mcptoolspec.totoollistasync() to get the list of executable tools and pass them directly to your FunctionAgent.

Q: Can LlamaIndex store Drata asset configurations in a vector database?

Yes, you can index the JSON output from dratalistassets directly into your vector store. This lets you run semantic queries across your cloud infrastructure configs to find compliance anomalies without calling the API repeatedly.

Q: How does LlamaIndex handle policy acknowledgment tracking with Drata?

The framework indexes policy metadata from dratalistpolicies and cross-references it with user status from dratalistpersonnel. This allows your agent to answer complex questions about which departments are lagging behind on their annual compliance signatures.

Q: Does this setup support the LlamaIndex allowedtools filter?

Yes, you can restrict your agent to specific tools like dratalisttests or dratalistframeworks using the framework's standard tool filtering. This limits the agent's scope during automated compliance checks.

Index live Drata compliance data into LlamaIndex vector stores to query your audit readiness 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 Drata MCP to LlamaIndex

Create your Vinkius account to connect Drata 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 Drata with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build compliance RAG engines with this MCP Server

To build compliance RAG databases, `drata_list_controls` retrieves your complete list of compliance requirements, which LlamaIndex immediately indexes as searchable document nodes. This allows your LlamaIndex RAG pipeline to perform semantic searches over your actual Drata control statuses rather than relying on static PDF files. When you ask a question about your security posture, the LlamaIndex engine queries the vector store and uses `drata_get_control` to fetch the live passing or failing state. Grounding your LlamaIndex agent's answers in real-time Drata API data stops compliance hallucinations before they reach your team.

Index cloud asset states for LlamaIndex search

By calling `drata_list_assets`, you export your monitored cloud infrastructure, which LlamaIndex indexes directly into your vector database to make your physical inventory searchable via natural language. If you need to find unencrypted resources or isolated VPCs, the LlamaIndex agent queries this local index first. The LlamaIndex agent then runs `drata_list_tests` to verify if those specific Drata resources are currently failing any automated continuous monitoring checks.

Query policy compliance and personnel training

To track documentation health, `drata_list_policies` pulls your Drata security policies, including CIS approvals and employee acknowledgment rates, into the LlamaIndex document store. The LlamaIndex agent matches these Drata policies against active personnel records fetched via `drata_list_personnel` to identify who has not signed the latest documents. By calling `drata_get_policy`, the LlamaIndex agent retrieves the exact version history and renewal dates. This MCP connection lets you query your overall Drata documentation status alongside live training milestones in LlamaIndex without manual spreadsheet tracking.

Setup guide

Set up Drata 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 Drata 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 Drata tools.",
)
response = await agent.run("List recent Drata 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 Drata. 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 Drata 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 Drata MCP in LlamaIndex

Use the `BasicMCPClient` to connect to the Vinkius endpoint, then wrap it with `McpToolSpec`. Call `mcp_tool_spec.to_tool_list_async()` to get the list of executable tools and pass them directly to your `FunctionAgent`.

Yes, you can index the JSON output from `drata_list_assets` directly into your vector store. This lets you run semantic queries across your cloud infrastructure configs to find compliance anomalies without calling the API repeatedly.

The framework indexes policy metadata from `drata_list_policies` and cross-references it with user status from `drata_list_personnel`. This allows your agent to answer complex questions about which departments are lagging behind on their annual compliance signatures.

Yes, you can restrict your agent to specific tools like `drata_list_tests` or `drata_list_frameworks` using the framework's standard tool filtering. This limits the agent's scope during automated compliance checks.

Your cloud asset configuration details, including VPC IDs and encryption states, are processed entirely in memory and never written to disk. The Vinkius platform routes these payloads through a zero-trust network that discards all data immediately after the API request completes.

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