How to Use the Halo Security MCP in LlamaIndex

Q: How does LlamaIndex avoid stale data when indexing Halo Security findings?

You can set up a query pipeline that calls listscans and listissues before running a retrieval step. This ensures your vector index is updated with the latest scan results before the LLM generates an answer.

Q: Can I build a Slack bot that queries Halo Security using LlamaIndex?

Absolutely. You can wrap the MCP tools in a LlamaIndex FunctionAgent that listens to Slack commands, queries getsecurityrisk or listopenports, and replies with the exact status of your external perimeter.

Q: Can I trigger a new scan from my query interface?

Yes, your agent can call triggerscan on demand if a user asks to re-evaluate a target. Once the scan is initiated, the agent can monitor its progress by checking listscans periodically.

Q: Where does the server process my target IP addresses and vulnerability details?

All target IP addresses and vulnerability data fetched via getissue are routed through an ephemeral V8 sandbox. We enforce a zero-trust architecture, meaning your sensitive network data is never cached or stored on Vinkius servers.

Index your external attack surface into LlamaIndex to query open ports, certificates, and risks using natural language.

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Connect Halo Security MCP to LlamaIndex

Create your Vinkius account to connect Halo Security 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 Halo Security with LlamaIndex

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Index live security posture for RAG

This integration lets you convert active security data from `list_targets` and `list_issues` into a queryable knowledge base. LlamaIndex ingests your actual security posture directly into its document store. Your agent can then answer complex questions about your vulnerability status using real-time data instead of relying on stale export files. When you query your index about outdated software, LlamaIndex pulls the latest technology stacks via `list_technologies`. It matches this data against known CVEs, giving you a localized, context-aware security advisor that actually knows your network.

Semantic search over Halo Security MCP findings

Stop digging through dashboards and use LlamaIndex to query the output of `list_open_ports` and `list_dns_records` directly. This allows you to search your infrastructure using natural phrases like 'which public servers are running database ports.' The framework maps these semantic queries directly to the structured outputs of the MCP Server. This means your team can find exposed services and verify DNS configurations without writing SQL or learning complex API query syntax.

Grounded risk assessments without hallucinations

Prevent LLM hallucinations by feeding raw metrics from `get_security_risk` and `list_certificates` directly into your index context. The agent drafts its security reports based strictly on the active certificates and risk scores returned by the API. If an executive asks for a summary of current liabilities, the agent fetches the issues via `list_issues` and formats them into a clean markdown table. Every claim the agent makes is backed by a specific issue ID retrieved during the session.

Setup guide

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

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Common questions about Halo Security MCP in LlamaIndex

You can set up a query pipeline that calls `list_scans` and `list_issues` before running a retrieval step. This ensures your vector index is updated with the latest scan results before the LLM generates an answer.

Absolutely. You can wrap the MCP tools in a LlamaIndex `FunctionAgent` that listens to Slack commands, queries `get_security_risk` or `list_open_ports`, and replies with the exact status of your external perimeter.

No. The MCP adapter handles schema conversion automatically. It translates the JSON outputs from tools like `list_certificates` and `list_targets` into documents that LlamaIndex can index immediately.

Yes, your agent can call `trigger_scan` on demand if a user asks to re-evaluate a target. Once the scan is initiated, the agent can monitor its progress by checking `list_scans` periodically.

All target IP addresses and vulnerability data fetched via `get_issue` are routed through an ephemeral V8 sandbox. We enforce a zero-trust architecture, meaning your sensitive network data is never cached or stored on Vinkius servers.

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