How to Use the Hudu MCP in LlamaIndex

Index your Hudu knowledge base into LlamaIndex for grounded, accurate AI answers.

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Connect Hudu MCP to LlamaIndex

Create your Vinkius account to connect Hudu 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 Hudu with LlamaIndex

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Perplexity

Semantic indexing for LlamaIndex

Turn your static documentation into searchable vector data. By passing results from `list_articles` directly into your index, you ensure your agent answers questions based on your specific company procedures. This creates a knowledge-augmented agent. It doesn't guess; it looks up the relevant article before providing a response.

Real-time data grounding

Sync your live Hudu environment with your RAG application. Whenever your agent needs the latest network topology, it executes `list_networks` to pull fresh data into the context window. Your answers are never stale. The agent queries the live API, ensuring that what you see in the chat matches what is currently documented.

Unified knowledge retrieval

Combine your documented assets with your indexed knowledge. You can search across both `list_websites` and your internal articles using a single interface. This gives your agent a complete picture. It understands both the structure of your infrastructure and the processes written to maintain it.

Setup guide

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

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Why Choose Vinkius

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Real-time monitoring

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

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place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Hudu MCP in LlamaIndex

You use the MCP tool spec to convert API outputs into a format LlamaIndex can ingest. The agent then performs semantic searches over that data.

Absolutely. By indexing your procedures and assets, you can ask the agent to explain the relationship between specific network segments and company policies.

You can configure an allowed tools filter. This restricts the agent to only the specific data subsets you want it to index.

Access is scoped to your specific API key. No data is cached by the server itself, keeping your records ephemeral and secure.

The server handles the error and returns a clean response. Your sensitive asset information is never exposed in raw stack traces.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python