How to Use the 42Crunch MCP in LlamaIndex

Q: Can 42Crunch reports be indexed by LlamaIndex?

Absolutely, just pass the output of getauditreport to your ingestion pipeline. The text-based audit data is ideal for semantic search and retrieval.

Index your security audit history into LlamaIndex for semantic search and grounded API intelligence.

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Connect 42Crunch MCP to LlamaIndex

Create your Vinkius account to connect 42Crunch 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 42Crunch with LlamaIndex

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Knowledge-augmented security with LlamaIndex

Feed your audit reports from `get_audit_report` directly into your vector store. Your agents stop guessing about past security issues and start querying the actual history of your API health. This indexing allows your LlamaIndex setup to answer questions about long-term vulnerability trends. You gain a searchable memory of your platform's security evolution.

Query your API metadata via LlamaIndex

Use `list_apis` to pull current security scores and sync them into your knowledge base. When you ask about the status of a specific service, the agent pulls the most recent data stored in your index. It keeps your documents and live API metadata in sync. You stop relying on stale documentation and start building RAG pipelines that know your current security posture.

Analyze conformance scans in LlamaIndex

Store `get_scan_report` results as documents within your index for deeper analysis. The agent correlates scan failures with code changes, providing context that basic dashboards miss. This MCP Server provides the raw conformance data that makes your LlamaIndex queries accurate. You get a unified view of your security standards.

Setup guide

Set up 42Crunch 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 42Crunch 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 42Crunch tools.",
)
response = await agent.run("List recent 42Crunch data")

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Common questions about 42Crunch MCP in LlamaIndex

You configure the tool output to be ingested by your vector indexers. The server provides the API metadata which LlamaIndex then converts into searchable embeddings.

Absolutely, just pass the output of `get_audit_report` to your ingestion pipeline. The text-based audit data is ideal for semantic search and retrieval.

By indexing scan results over time, you create a history of API conformance. Your agent can then identify patterns where specific endpoints repeatedly fail security checks.

You automate the process by chaining the tool calls to your ingestion scripts. Once set up, your agent handles the updates whenever you trigger a new audit.

Data access is restricted to the credentials you provide to the server. Your sensitive API specifications are only processed through encrypted channels and stored in your private vector database.

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