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

Q: Can I index KnowBe4 (KMSAT Reporting) tool outputs for offline RAG in LlamaIndex?

Yes, you can load data from getuserdetails and store it in a vector index. This allows you to run semantic search over user profiles and training records without calling the API every time.

Index live KnowBe4 (KMSAT Reporting) metrics directly into your LlamaIndex vector store for instant, grounded security RAG.

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Connect KnowBe4 (KMSAT Reporting) MCP to LlamaIndex

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

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Build a searchable security index with LlamaIndex

`list_users` fetches your entire user directory, which the framework indexes directly into a vector store. The framework converts raw user metadata into searchable nodes so you can query your roster using natural language. This setup turns static API outputs into a dynamic knowledge base. Your agent queries this index to find specific profiles without hitting the API repeatedly.

Ground agent responses in real-time risk data

`get_account_risk_score` provides the baseline risk metric that grounds your security RAG pipeline. The agent pulls this score and combines it with document data to answer complex compliance questions. This prevents the model from hallucinating numbers during executive reviews. Every response relies on the actual, current risk score fetched directly from your KMSAT account.

Analyze training campaigns using an MCP Server

`list_training_campaigns` retrieves the status of all active security courses across your organization. The agent indexes these campaigns alongside your internal HR policies to find gaps in training coverage. You can then query the index to see if your active campaigns cover the topics outlined in your security policy. The agent uses `get_training_campaign_details` to verify the specifics of each course.

Setup guide

Set up KnowBe4 (KMSAT Reporting) 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 KnowBe4 (KMSAT Reporting) 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 KnowBe4 (KMSAT Reporting) tools.",
)
response = await agent.run("List recent KnowBe4 (KMSAT Reporting) data")

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Common questions about KnowBe4 (KMSAT Reporting) MCP in LlamaIndex

You initialize the MCP client in your Python code and convert the tools into LlamaIndex tool specs. This lets your function-calling agents query tools like `list_phishing_tests` to build their context.

Yes, you can load data from `get_user_details` and store it in a vector index. This allows you to run semantic search over user profiles and training records without calling the API every time.

The framework uses query engines to determine when to call tools like `list_groups` versus when to search the local index. This ensures your agent always uses the most efficient data retrieval path.

Yes, you can use the allowed tools filter when defining your `McpToolSpec`. This lets you restrict the agent to read-only tools like `get_training_campaign_details` for safety.

The server processes sensitive information like employee email addresses, phishing test failure rates, and group memberships inside ephemeral V8 isolates. Vinkius secures your credential storage, keeping your API keys isolated from the runtime environment.

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