How to Use the Drata MCP in Pydantic AI

Build type-safe compliance pipelines in Pydantic AI using this Drata MCP Server to validate incoming schemas.

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Connect Drata MCP to Pydantic AI

Create your Vinkius account to connect Drata 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 Drata with Pydantic AI

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Validate compliance controls with Pydantic AI

Enforce strict schema validation whenever your agent fetches control data using `drata_list_controls` to avoid silent compliance failures. By using this Drata MCP Server, your Pydantic AI agents validate every compliance payload at runtime. If the API response structure changes or contains unexpected null values, the runtime validation catches it instantly. Your agent can inspect failing controls with `drata_get_control` knowing the data matches your exact type definitions.

Type-safe personnel and device tracking

Pull live employee records using `drata_list_personnel` to map them directly to strongly-typed Python models. Tracking employee compliance status requires absolute accuracy, and this setup keeps your data clean. Your agent can safely evaluate MDM enrollment and training milestones via `drata_get_person`. If an employee record contains incomplete security details, the framework raises a validation error instead of letting the agent make decisions on corrupt data.

Audit policy documents with strict schema enforcement

Fetch active security documents using `drata_list_policies` to ensure that review cycles and ownership fields are fully populated. Policy management is too critical for loose typing, so your agent enforces strict schema checks. By querying `drata_get_policy`, the agent extracts version histories and acknowledgment rates. The type-safe execution guarantees that your compliance dashboards always receive clean, structured data.

Setup guide

Set up Drata 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": {
        "drata-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Drata transactions")
print(result.output)

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Common questions about Drata MCP in Pydantic AI

Initialize the connection using the toolset helper with your Vinkius HTTP endpoint. Pass that instance directly into the toolsets list of your Agent.

Pydantic AI validates all incoming schemas at runtime. If an endpoint returns unexpected fields, the framework raises a validation error immediately rather than passing bad data to the model.

Yes. Because the framework is model-agnostic, you can connect your local models to the MCP Server to query vendors and run local risk assessments.

Yes. The toolset fully supports async execution, allowing your compliance pipeline to query multiple endpoints in parallel without blocking.

All data fetched from your policies and vendor risk assessments is transmitted over secure TLS connections directly to your local runtime. Vinkius operates as an ephemeral proxy, never storing your compliance data.

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