Checkmarx MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Checkmarx as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Checkmarx. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Checkmarx?"
)
print(response)
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.
LlamaIndex agents combine Checkmarx tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Checkmarx MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Checkmarx MCP Server
LlamaIndex provides unique advantages when paired with Checkmarx through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Checkmarx tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Checkmarx tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Checkmarx, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Checkmarx tools were called, what data was returned, and how it influenced the final answer
Checkmarx + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Checkmarx MCP Server delivers measurable value.
Hybrid search: combine Checkmarx real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Checkmarx to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Checkmarx for fresh data
Analytical workflows: chain Checkmarx queries with LlamaIndex's data connectors to build multi-source analytical reports
Checkmarx MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Checkmarx to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Checkmarx to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCheckmarx + LlamaIndex FAQ
Common questions about integrating Checkmarx MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.