How to Use the Veracode MCP in LlamaIndex

Q: Which tools are best for indexing with Veracode and LlamaIndex?

For comprehensive knowledge, index listsecurityfindings results. This retrieves unified security findings that cover multiple types of issues, giving your vector store the broadest possible scope.

Q: Can I track changes to application profiles using Veracode and LlamaIndex?

Yes. By indexing historical records of getapplicationdetails, you can query how an app's deployment state or business criticality rating has changed over time, providing a useful audit trail.

Q: What happens if I list all applications with Veracode and LlamaIndex?

Running listapplications provides the foundational GUID for every resource. Indexing this allows your RAG app to serve as a directory, letting users search by name or purpose rather than needing the exact ID.

Index security results from Veracode into LlamaIndex knowledge bases for RAG applications.

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Connect Veracode MCP to LlamaIndex

Create your Vinkius account to connect Veracode to LlamaIndex 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 Veracode with LlamaIndex

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Search historical compliance reports.

Instead of just calling `get_application_details`, you index the returned policy status, criticality rating, and risk scores. This lets your LlamaIndex application answer questions like, 'Which applications are marked high-risk but haven't been audited in 6 months?'

Build a knowledge base of vulnerabilities.

Use `get_finding_details` to pull specific flaw data—the CWE type, file path, and remediation guidance. Indexing this transforms raw findings into searchable knowledge. You can query past sessions to recall exactly how you handled a certain class of vulnerability.

Query all configured security scans.

The `list_dynamic_analyses` tool provides a list of active DAST scans. By indexing this output, your RAG application can answer complex questions like, 'Show me all the dynamic analyses that covered the payment processing module last quarter.' It grounds answers in live API data.

Setup guide

Set up Veracode MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Veracode MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Veracode tools.",
)
response = await agent.run("List recent Veracode data")

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Common questions about Veracode MCP in LlamaIndex

LlamaIndex indexes tool outputs, turning them into a searchable vector store. You can query past configurations or security reports and get answers grounded in the actual data from the MCP Server, eliminating hallucinations.

For comprehensive knowledge, index `list_security_findings` results. This retrieves unified security findings that cover multiple types of issues, giving your vector store the broadest possible scope.

Yes. By indexing historical records of `get_application_details`, you can query how an app's deployment state or business criticality rating has changed over time, providing a useful audit trail.

Running `list_applications` provides the foundational GUID for every resource. Indexing this allows your RAG app to serve as a directory, letting users search by name or purpose rather than needing the exact ID.

This MCP Server handles source code flaws (finding details) and application profile status. Always ensure your index is scoped only to necessary, non-PII security metrics.

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