How can the AI help me fix a vulnerability faster?

Once an issue is identified via scan results, ask your agent to pull the 'Best Fix Location' (BFL) using the query ID. Checkmarx mathematically finds the common root code block, and your AI can instantly rewrite that exact block to sanitize the flaw. You save hours tracing code paths.

Can the agent initiate a static code scan independently?

Yes! Tell the agent to 'Run a scan on project ID X targeting the main branch'. It initiates the analysis array natively across Checkmarx One engines. You can poll for completion status later and retrieve the new dataset directly via chat.

Does it segregate AppSec results from Cloud infrastructure flaws?

It does. Application flaws are pulled cleanly via `get_scan_results`, whereas misconfigurations tied to Docker, Kubernetes, or Terraform limits use a dedicated `get_kics_results` pipeline. The agent intrinsically separates the context for your DevOps team.

Checkmarx MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Checkmarx
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Checkmarx through Vinkius, pass the Edge URL in the `mcps` parameter and every Checkmarx tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Checkmarx Specialist",
    goal="Help users interact with Checkmarx effectively",
    backstory=(
        "You are an expert at leveraging Checkmarx tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Checkmarx "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

When paired with CrewAI, Checkmarx becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Checkmarx tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Checkmarx MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Checkmarx

Why Use CrewAI with the Checkmarx MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Checkmarx through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Checkmarx + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Checkmarx MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Checkmarx for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Checkmarx, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Checkmarx tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Checkmarx against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Checkmarx MCP Tools for CrewAI (10)

These 10 tools become available when you connect Checkmarx to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Checkmarx to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Checkmarx + CrewAI FAQ

Common questions about integrating Checkmarx MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.