How to Use the Drata MCP in LangChain

Q: How do I pass Drata tools to a LangChain agent using this MCP Server?

Initialize the MultiServerMCPClient with the Vinkius transport URL to register the tools. Call client.gettools() to retrieve the schema-mapped list and pass them directly into your createagent call. This lets your LangChain agent dynamically select tools like dratalisttests during execution.

Q: Can LangChain trace Drata API latency and token usage?

Yes, every tool call like dratalistcontrols integrates with LangSmith tracing. You can view the exact inputs, outputs, and execution times of your compliance chains in the LangSmith dashboard. This provides full observability into how your agent interacts with your compliance data.

Q: How does this MCP Server handle multi-framework audits in LangChain?

Your agent can query dratalistframeworks to evaluate overall readiness scores across SOC 2 and ISO 27001. The LangChain chain then uses those scores to decide whether to pull failing administrative policies via dratalistpolicies or technical infrastructure assets.

Chain compliance checks directly into your LangChain agents to automate SOC 2 readiness without manual exports.

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Connect Drata MCP to LangChain

Create your Vinkius account to connect Drata to LangChain 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 Drata with LangChain

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Run multi-step compliance chains in LangChain

The `drata_list_controls` tool pulls your current Drata control failures straight into your LangChain runnable sequence. From there, your LangChain agent parses the failing compliance requirements and immediately triggers `drata_list_tests` to pinpoint the exact broken cloud monitors. This LangChain compliance chain links the high-level Drata compliance gap to the raw technical test failures in one execution pass. This MCP integration lets you track this entire multi-step reasoning flow inside LangSmith to observe latency and token costs for every Drata API call. When a Drata control fails, the LangChain agent doesn't stop; it feeds that output into `drata_list_assets` to isolate the unencrypted S3 buckets or open ports causing the alert.

Automate HR offboarding checks with this MCP Server

To audit your team roster, `drata_list_personnel` pulls your personnel data directly into a LangChain ReAct agent to find employees with overdue security training or missing device compliance. When the LangChain agent identifies a non-compliant user, it passes their unique identifier to `drata_get_person` to extract their specific MDM enrollment status and linked identity provider groups. This LangChain mapping allows your chain to flag Drata compliance gaps before an auditor spots them. By feeding this live Drata personnel data directly into downstream LangChain runnables, you cut out the manual chasing entirely.

Chain vendor risk reviews and policy updates

For third-party risk tracking, `drata_list_vendors` retrieves your complete vendor roster, allowing your LangChain agent to evaluate questionnaire completion states and SOC 2 report reviews. Your LangChain agent evaluates these Drata vendor risks and uses `drata_get_policy` to verify if your vendor management policies require an annual review or owner update. If a critical Drata vendor lacks a current SOC 2 review, the LangChain agent flags this gap against your active frameworks pulled via `drata_list_frameworks`. This LangChain setup turns static Drata vendor tracking into an active, chain-driven compliance guardrail.

Setup guide

Set up Drata MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Drata tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "drata-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Drata transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Drata MCP in LangChain

Initialize the `MultiServerMCPClient` with the Vinkius transport URL to register the tools. Call `client.get_tools()` to retrieve the schema-mapped list and pass them directly into your `create_agent` call. This lets your LangChain agent dynamically select tools like `drata_list_tests` during execution.

Yes, every tool call like `drata_list_controls` integrates with LangSmith tracing. You can view the exact inputs, outputs, and execution times of your compliance chains in the LangSmith dashboard. This provides full observability into how your agent interacts with your compliance data.

Your agent can query `drata_list_frameworks` to evaluate overall readiness scores across SOC 2 and ISO 27001. The LangChain chain then uses those scores to decide whether to pull failing administrative policies via `drata_list_policies` or technical infrastructure assets.

The agent cannot write changes back to Drata, but it can identify the exact issues. By calling `drata_list_tests` and mapping the failing resources, your chain can output precise remediation instructions for your engineering team.

This server processes sensitive personnel records, including background check dates and MDM status, within a secure, ephemeral V8 isolate sandbox. No compliance data is stored on Vinkius servers, and all API calls to Drata endpoints occur over encrypted channels using your single endpoint token.

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