How to Use the Lacework (Cloud Security & CNAPP) MCP in Pydantic AI

Q: Can I define custom Pydantic AI schemas for Lacework (Cloud Security & CNAPP) tool outputs?

Yes. You can wrap tools like searchcloudinventory or listsecuritypolicies in custom agent functions that validate the raw JSON output against your specific Pydantic models.

Build type-safe security agents with Pydantic AI to validate Lacework alerts and vulnerabilities at runtime.

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Connect Lacework (Cloud Security & CNAPP) MCP to Pydantic AI

Create your Vinkius account to connect Lacework (Cloud Security & CNAPP) 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 Lacework (Cloud Security & CNAPP) with Pydantic AI

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Strict Pydantic AI validation for cloud alerts

Stop letting malformed API responses break your security pipelines. This MCP Server integrates with Pydantic AI, forcing every alert payload retrieved via `get_alert` or `search_alerts` to conform to strict Python type definitions. If the server returns unexpected metadata—like a missing AWS account ID or an unformatted container SHA—the framework fails loudly immediately. This prevents your agent from acting on corrupt or incomplete security telemetry.

Type-safe vulnerability scanning using Pydantic AI

Ensure your CVE tracking scripts always receive validated data. Your Pydantic AI agent calls `list_host_vulnerabilities` and `search_cve_exposure` through the MCP Server to locate systems running compromised processes. Pydantic AI validates the structure of the returned CVE lists against your defined schemas. You can safely automate remediation scripts, knowing the target machine IDs and package names are guaranteed to be correct.

Validate LQL query schemas before execution

Run complex threat hunting operations without risking silent failures. The agent uses `list_lql_queries` to inspect active Lacework Query Language structures, verifying their syntax before calling `execute_query`. Because every field is strictly typed, your code catches schema mismatches in the query output before they can pollute your downstream security dashboards or SIEM integrations.

Setup guide

Set up Lacework (Cloud Security & CNAPP) 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": {
        "lacework-cloud-security-cnapp-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Lacework (Cloud Security & CNAPP) transactions")
print(result.output)

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Common questions about Lacework (Cloud Security & CNAPP) MCP in Pydantic AI

Use the MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your Agent's toolsets parameter to enable type-safe security tool execution.

The framework raises a ValidationError immediately. This prevents your agent from processing partial or corrupted alert data, ensuring your security automations only run on validated payloads.

Yes. You can wrap tools like `search_cloud_inventory` or `list_security_policies` in custom agent functions that validate the raw JSON output against your specific Pydantic models.

Use the Streamable HTTP transport provided by Vinkius. It connects securely to the hosted server without requiring you to manage local node processes or complex SSH tunnels.

The server processes cloud inventory details, vulnerability lists, and alert telemetry. Vinkius isolates this MCP Server traffic within a zero-trust V8 sandbox, ensuring your credentials are never exposed to the LLM or third parties.

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