How to Use the KnowBe4 (KMSAT Reporting) MCP in LangChain

Q: Can LangChain run sequential chains across multiple KnowBe4 (KMSAT Reporting) tools?

Yes, the agent uses ReAct reasoning to feed the output of getaccountriskscore into subsequent tool calls. It handles multi-step processes like pulling user lists and checking their specific groups automatically.

Build LangChain reasoning loops that query KnowBe4 (KMSAT Reporting) data via our managed MCP Server.

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Connect KnowBe4 (KMSAT Reporting) MCP to LangChain

Create your Vinkius account to connect KnowBe4 (KMSAT Reporting) 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 KnowBe4 (KMSAT Reporting) with LangChain

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Run multi-stage security compliance chains with LangChain

`list_users` pulls your entire active directory roster from KnowBe4 to feed directly into the next step of your chain. Your agent takes this list, extracts the emails, and immediately feeds them into `list_user_groups` to map out your organizational layout. LangSmith traces the entire sequence, showing you exactly how raw data flows from one tool to the next. You see the exact inputs, execution times, and outputs without guessing why an agent made a specific decision.

Automate risk-based remediation loops

`get_account_risk_score` gives your agent the exact risk metric for your organization. If that score spikes, the agent triggers a sub-chain that queries `list_phishing_tests` to pinpoint where the failures happened. The agent then pulls specific test profiles using `get_phishing_test_details` to isolate the exact templates that fooled your team. This lets you automate targeted alerts instead of sending generic warnings to everyone.

Audit training gaps across groups

`list_groups` retrieves every department segment in your account so your agent can cross-reference them with active assignments. The agent loops through each group and calls `list_training_campaigns` to verify who is lagging behind on their security modules. Passing this raw JSON directly into your LLM chain lets you generate custom, group-specific reminders. You get structured markdown reports showing exactly which department needs immediate intervention.

Setup guide

Set up KnowBe4 (KMSAT Reporting) 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 KnowBe4 (KMSAT Reporting) 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({
    "knowbe4-kmsat-reporting-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 KnowBe4 (KMSAT Reporting) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about KnowBe4 (KMSAT Reporting) MCP in LangChain

Install the LangChain MCP adapter package and instantiate the multi-server client pointing to your Vinkius endpoint. Register the tools with your agent executor to let the model call endpoints like `list_users` dynamically.

Yes, the agent uses ReAct reasoning to feed the output of `get_account_risk_score` into subsequent tool calls. It handles multi-step processes like pulling user lists and checking their specific groups automatically.

LangSmith records every single API call made by your agent, including parameters sent to `get_user_details`. You can inspect the exact payload, latency, and token cost for every security audit run.

You define the allowed tool list when initializing your LangChain agent. If you only want to allow auditing, pass `list_training_campaigns` and omit any tools you want to restrict.

The server only reads data like user emails, training statuses, and risk scores via a secure V8 sandbox on Vinkius. Your API keys are stored as environment variables and never exposed to the LLM or external logs.

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