How to Use the Hudu MCP in LangChain

Q: Can LangChain use Hudu to automate ticket routing?

Yes. You can chain listassets and listcontacts to determine which team owns an asset, then route the issue accordingly.

Build reasoning chains that pull live IT documentation directly into your LangChain workflows.

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Connect Hudu MCP to LangChain

Create your Vinkius account to connect Hudu to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Dynamic IT data piping with LangChain

Chain your Hudu data directly into your agent's reasoning loop. You define the sequence where `list_assets` feeds into a diagnostic step, allowing your agent to make decisions based on real-time inventory. Your agent handles the logic of which tool to trigger next. By connecting this MCP Server, your pipeline gains direct access to your documentation without manual lookups.

Traceable documentation queries

Monitor every request your LangChain agent makes to your Hudu instance. Every call to `list_passwords` or `list_procedures` shows up in your execution traces, showing exactly what data influenced the agent's output. Debugging becomes a matter of checking the logs. You see exactly what the agent pulled and when, keeping your infrastructure audit-ready at all times.

Multi-step documentation analysis

Combine your IT records with other data sources in a single chain. Your agent can pull a company profile via `get_company` and cross-reference it with external database records in one go. This setup removes the need for custom API glue code. You just pipe the output of one tool into the input of another, creating a fluid data flow.

Setup guide

Set up Hudu MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Hudu tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "hudu-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Hudu transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Hudu MCP in LangChain

Install the MCP adapters and point the client to your server URL. You then fetch the tool list and inject them into your agent definition.

Yes. You can chain `list_assets` and `list_contacts` to determine which team owns an asset, then route the issue accordingly.

It does. Use the client session method to maintain context across multiple turns of a conversation.

Your API key stays local to the server process. We use an endpoint token to bridge your agent to the data.

The server only reads the specific fields you request. We never store your credentials or documentation, ensuring your sensitive asset lists remain within your control.

Use it with your favorite AI tools

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