Checkmarx MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Checkmarx through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Checkmarx "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Checkmarx?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Checkmarx MCP Server
Connect your Checkmarx One enterprise environment to any AI agent and take programmatic control over your Application Security posture. Analyze deep code flaws through natural chat instead of navigating complex cyber dashboards.
Pydantic AI validates every Checkmarx tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Projects & Applications — Inventory your codebase containers, inspect active project linkages, and prepare specific branches for security scanning
- Scans Lifecycle — Trigger dynamic SAST/SCA security scans on repos, cancel redundant queues, and poll engines for precise execution timing
- Vulnerability Triage — Extract core datasets of severe vulnerabilities, mapping exact lines of code where the flawed logic resides
- Best Fix Location (BFL) — Ask the agent to calculate the exact optimal spot in your execution path to apply a patch that resolves the flaw entirely
- KICS (IaC) — Read specialized Infrastructure as Code metrics isolating misconfigurations exclusively in Terraform, Dockerfiles, or Kubernetes YAML
The Checkmarx MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Checkmarx to Pydantic AI via MCP
Follow these steps to integrate the Checkmarx MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Checkmarx with type-safe schemas
Why Use Pydantic AI with the Checkmarx MCP Server
Pydantic AI provides unique advantages when paired with Checkmarx through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Checkmarx integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Checkmarx connection logic from agent behavior for testable, maintainable code
Checkmarx + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Checkmarx MCP Server delivers measurable value.
Type-safe data pipelines: query Checkmarx with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Checkmarx tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Checkmarx and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Checkmarx responses and write comprehensive agent tests
Checkmarx MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Checkmarx to Pydantic AI via MCP:
cancel_scan
Prevents unnecessary engine resource consumption and drops the scanning context if the developer pushed a new commit overlapping the running job. Cancel an actively running Checkmarx scan
get_kics_results
Focuses solely on Terraform, CloudFormation, Kubernetes YAML, and Dockerfile misconfigurations rather than typical application source code flaws. Get specialized Infrastructure as Code (KICS) findings
get_project
Essential for ensuring the correct branch and source control context is selected before triggering new scans. Get details for a specific Checkmarx project
get_scan_details
It returns granular execution details including which scan engines (SAST, SCA, KICS) were fired, their individual execution timings, and any engine-specific failure reasons. Check the precise status and configuration of a Checkmarx scan
get_scan_results
Each result includes the vulnerability severity, state (To Verify, Confirmed, Urgent), description, and the exact lines of code where the flaw was detected. Requires a completed scan ID. Download SAST and security vulnerability findings for a scan
list_applications
An Application acts as an overarching container for multiple individual microservices or projects, providing aggregated risk reporting and security metric visibility across a logical product. List Checkmarx One Applications
list_bfl
Provide the scan ID and the specific query (rule) ID string. Get Best Fix Location (BFL) for a specific vulnerability node
list_projects
A Project represents a specific codebase. Includes project metadata, IDs, and assigned application linkages. List all Checkmarx One Projects
list_scans
Includes the scan ID, current status (Completed, Running, Failed, Canceled), branch targeted, and timestamps. Use the scan ID to fetch the actual vulnerability results. List all historical and active scans for a Checkmarx project
run_scan
Extensively used in CI/CD integrations to assert security quality on PRs. Returns the ID of the newly queued scan. Trigger a new Checkmarx One code scan
Example Prompts for Checkmarx in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Checkmarx immediately.
"List the most severe vulnerabilities found in the last Checkmarx scan."
"Trigger a new SAST scan for my current Checkmarx project."
"How do I fix the SQL injection vulnerability found in the Checkmarx report?"
Troubleshooting Checkmarx MCP Server with Pydantic AI
Common issues when connecting Checkmarx to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiCheckmarx + Pydantic AI FAQ
Common questions about integrating Checkmarx MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Checkmarx with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Checkmarx to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.