How to Use the Checkmarx MCP in Pydantic AI

Q: Does Checkmarx work with Pydantic AI for infrastructure security?

Yes. You can call getkicsresults to fetch infrastructure misconfigurations, which are then validated by your Pydantic models before the agent acts.

Enforce strict security validation in Pydantic AI using the Checkmarx MCP Server.

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Connect Checkmarx MCP to Pydantic AI

Create your Vinkius account to connect Checkmarx 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 Checkmarx with Pydantic AI

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Type-safe security scans for Pydantic AI

Every response from the server is validated against your Pydantic models. When your agent calls `list_scans`, the data must match your schema or the process halts. This prevents malformed scan data from poisoning your agent's logic. You get reliable execution when triggering `run_scan` or checking job status.

Accurate vulnerability tracking in Pydantic AI

Use `get_scan_results` to pull actionable security data. Because you use Pydantic models, you know exactly what fields are present in every vulnerability node. If the server returns unexpected data, the agent fails immediately. This is critical for maintaining high security standards during your vulnerability triage.

Targeted remediation with Pydantic AI

Find the best fix for any issue by calling `list_bfl` with the vulnerability ID. Your agent receives a validated path to the fix location. Manage your project hierarchy using `list_projects` and `list_applications`. It keeps your security data model clean, predictable, and fully type-checked.

Setup guide

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

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

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

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Common questions about Checkmarx MCP in Pydantic AI

Install the slim package, then use the MCPToolset class to connect to your server. Add the toolset to your agent to start executing commands.

Yes. Every tool response is validated against your Pydantic models. If the server returns incorrect data, your agent throws a validation error.

The server shares scan status, vulnerability severity, and file locations. It keeps your sensitive source code out of the agent's memory.

Because you define strict models, your agent only processes the security findings you explicitly allow. It rejects anything that doesn't match your schema.

Yes. You can call `get_kics_results` to fetch infrastructure misconfigurations, which are then validated by your Pydantic models before the agent acts.

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