How to Use the Duda MCP in LangChain

Q: How do I pass Duda site lists into a LangChain run?

You run listdudasites to fetch the active site array, then pass that array directly as the input for your next chain step. LangChain handles the list formatting automatically, letting your agent process each site ID sequentially.

Q: Can a LangChain agent audit site status automatically?

Yes. Your agent calls listpublishedwebsites to filter active sites, then runs quicksitehealthaudit on each. This chain lets you monitor publishing rates across your entire portfolio without manual checks.

Q: How does LangChain handle Duda client profile lookups?

The agent uses listclientaccounts to find the correct ID, then calls getclientaccountprofile to pull permissions. You can trace this entire tool path inside LangSmith to verify execution speed.

Q: How do I inspect dynamic collections with this setup?

Call listsitecollections with a specific site ID. Your agent reads the raw schema of the dynamic collections, making it easy to verify data structures before running content updates.

Chain together Duda site audits and client profile checks directly inside your LangChain reasoning loops.

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Setup Duda with LangChain

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Automated multi-step site health audits

Build LangChain runs that check site health and flag issues instantly. Your agent starts by calling `list_duda_sites` to grab your entire inventory, then feeds those IDs directly into `quick_site_health_audit` to spot inactive sites. This MCP setup passes the output of one tool straight to the next in a single execution chain. You get clean, structured data on site activity without writing glue code or manually parsing nested payloads.

Client-specific template matching in chains

LangChain agents can match client accounts with the right templates using live data. The chain runs `get_client_account_profile` to verify permissions, then pulls options via `list_site_templates` to suggest the best starting point. Because LangSmith traces every step of this MCP Server interaction, you see exactly how the agent decided to pair a client with a specific template design.

Smart domain lookup and details retrieval

When a client asks about a site, your agent uses `search_sites_by_domain` to locate the target instantly. Once found, it immediately triggers `get_site_details` to pull configuration settings and active features. By linking these two tools in a LangGraph state machine, you eliminate manual dashboard searches. Your agent handles the lookup and returns the exact site data in seconds.

Setup guide

Set up Duda 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 Duda 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({
    "duda-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 Duda transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Duda MCP in LangChain

You run `list_duda_sites` to fetch the active site array, then pass that array directly as the input for your next chain step. LangChain handles the list formatting automatically, letting your agent process each site ID sequentially.

Yes. Your agent calls `list_published_websites` to filter active sites, then runs `quick_site_health_audit` on each. This chain lets you monitor publishing rates across your entire portfolio without manual checks.

The agent uses `list_client_accounts` to find the correct ID, then calls `get_client_account_profile` to pull permissions. You can trace this entire tool path inside LangSmith to verify execution speed.

Call `list_site_collections` with a specific site ID. Your agent reads the raw schema of the dynamic collections, making it easy to verify data structures before running content updates.

Your API credentials never pass through external servers. Vinkius runs the server in an isolated sandbox, meaning your client account profiles and site configurations stay encrypted and ephemeral during execution.

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