Veracode 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 Veracode 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 Veracode. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Veracode?"
)
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 Veracode MCP Server
Equip your AI agent with complete read and write access to your Veracode ecosystem. Seamlessly blend application security posture management alongside your typical development workflow using entirely conversational AI.
LlamaIndex agents combine Veracode 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
- Unified Vulnerability Tracing — Ask the agent to list Open security findings or mitigation statuses spanning across Static (SAST), Dynamic (DAST), and Component (SCA) analytics per application.
- Deep Flaw Details — Input specific Finding IDs and let the bot explain the underlying CWE error, affected code strings, severity ratings, and automated remediation tutorials.
- Portfolio AppSec Management — List tracked applications, create novel application profiles on the fly before a commit, or request health checks mapping sandbox testing environments.
- Dynamic Scan Queries — Poll your AI intuitively ensuring it retrieves the real-time execution bounds of your scheduled Web Application Security runtime scenarios.
The Veracode 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 Veracode to LlamaIndex via MCP
Follow these steps to integrate the Veracode 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 Veracode
Why Use LlamaIndex with the Veracode MCP Server
LlamaIndex provides unique advantages when paired with Veracode through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Veracode tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Veracode tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Veracode, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Veracode tools were called, what data was returned, and how it influenced the final answer
Veracode + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Veracode MCP Server delivers measurable value.
Hybrid search: combine Veracode real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Veracode 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 Veracode for fresh data
Analytical workflows: chain Veracode queries with LlamaIndex's data connectors to build multi-source analytical reports
Veracode MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Veracode to LlamaIndex via MCP:
create_application
Provide the app schema and profile name as a JSON string. Create a new Veracode application profile container
delete_application
This action is irreversible. Delete a Veracode application permanently
get_api_health
Check the health of Veracode connection
get_application_details
Information includes its Veracode compliance policy status, business criticality rating, deployment state, and risk scores. Get a detailed profile of a Veracode application
get_finding_details
Explains the vulnerability type (CWE), affected source file, code path, and remediation guidance. Get precise vulnerability details for a specific flaw/finding
list_applications
Most structural entities return a globally unique GUID which is required for sub-resource lookups. List all Veracode AppSec Applications
list_dynamic_analyses
List configured Dynamic Analysis (DAST) scans
list_sandboxes
List all testing sandboxes linked to an application
list_security_findings
Retrieve the unified security findings for an application
list_veracode_users
Used to manage RBAC roles. List authorized Veracode identity users
Example Prompts for Veracode in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Veracode immediately.
"List all applications currently monitored in our Veracode account."
"Get the detailed security profile for the application GUID 'f3b9...'."
"Explain finding ID '89' from that app and how to fix it."
Troubleshooting Veracode MCP Server with LlamaIndex
Common issues when connecting Veracode to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVeracode + LlamaIndex FAQ
Common questions about integrating Veracode 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 Veracode 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 Veracode to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.