How to Use the KnowBe4 (KMSAT Reporting) MCP in Pydantic AI

Type-safe security reporting for your Pydantic AI agents with the KnowBe4 (KMSAT Reporting) MCP Server.

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Connect KnowBe4 (KMSAT Reporting) MCP to Pydantic AI

Create your Vinkius account to connect KnowBe4 (KMSAT Reporting) to Pydantic AI 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 Pydantic AI

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Perplexity

Strictly typed security reporting

Avoid runtime surprises when processing security metrics. Every response from `get_account_risk_score` is validated against your Pydantic models, ensuring your agent never processes malformed data. If the API returns unexpected values, the agent fails immediately. This guarantees that your security reports are based on verified, clean data every single time.

Validate phishing test data

Use `list_phishing_store_results` to fetch campaign outcomes for your agent to review. Pydantic AI enforces the schema of these results, so your agent's logic remains predictable. It prevents hallucinations where an agent might invent fields. You get a reliable interface to the phishing data that your team trusts for auditing.

Audit user status with confidence

Keep your user database synchronized with your training platform. `list_users` returns data that is immediately parsed into your defined types, making it easy to spot discrepancies. Your agents cross-reference this against active groups to ensure training assignments are correct. It eliminates silent data errors that plague typical reporting scripts.

Setup guide

Set up KnowBe4 (KMSAT Reporting) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "knowbe4-kmsat-reporting-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to KnowBe4 (KMSAT Reporting) tools.",
)

result = await agent.run("List recent KnowBe4 (KMSAT Reporting) transactions")
print(result.output)

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Common questions about KnowBe4 (KMSAT Reporting) MCP in Pydantic AI

The framework validates every MCP response against your schema at runtime. If the API output doesn't match your Pydantic model, the process halts, preventing corrupt data from entering your agent's logic.

Yes. You define custom validation errors that trigger specific agent behaviors. If a tool fails to return data, your agent catches the exception and reports the issue clearly.

It is. Because the server is model-agnostic, you connect it to any LLM you are running locally or in the cloud. The type-safety remains consistent regardless of the model provider.

Install the slim package, point the toolset to the Vinkius URL, and inject the server into your agent. The tools are immediately available for use within your type-safe agent system.

The server retrieves email addresses as strings, which are then validated against your regex patterns in Pydantic. Access is restricted to read-only operations to protect sensitive user PII.

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