How to Use the HTML DOM Query Engine MCP in LangChain

Build deterministic extraction chains in LangChain with the HTML DOM Query Engine MCP Server.

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Connect HTML DOM Query Engine MCP to LangChain

Create your Vinkius account to connect HTML DOM Query Engine 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 HTML DOM Query Engine with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chainable DOM extraction for LangChain

Pipe raw HTML directly into your agentic pipelines. The `query_dom` tool accepts a string and returns specific nodes, allowing your chain to pass the result immediately to a downstream prompt without extra processing. Memory overhead stays low even with massive payloads. By offloading parsing to this MCP Server, your agents avoid the latency spikes common with heavier browser-based automation tools.

Deterministic data flow in LangChain

Standardize how your chains retrieve attributes or text. Using `query_dom` ensures that your agent receives consistent JSON output every time, removing the ambiguity that comes with fuzzy web scraping. Integrate this with your existing LangSmith tracing to monitor extraction latency. You'll see exactly how fast your chain pulls specific data points from target pages.

Multi-server aggregation for LangChain

Combine this MCP Server with your vector stores and database tools in a single LangChain agent. Your agent can fetch a URL, extract content with `query_dom`, and store the result in a database within one execution loop. This setup keeps your logic centralized. You define the flow, and the agent handles the extraction steps as discrete tool calls in your chain.

Setup guide

Set up HTML DOM Query Engine 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 HTML DOM Query Engine 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({
    "html-dom-query-engine-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 HTML DOM Query Engine transactions"
    })
    print(result["messages"][-1].content)

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Common questions about HTML DOM Query Engine MCP in LangChain

Install the necessary adapters and initialize the client using the provided URL. Once connected, call the tool discovery function to pass `query_dom` into your agent's toolset. It's ready to use in your next chain execution.

Yes. While the server is stateless, you can use the client session to maintain context across multiple turns. This ensures your agent keeps track of previous extraction results during a conversation.

Absolutely. The tool supports standard CSS selectors, letting you target deeply nested elements just like you would in a browser. It executes the query on the server side and returns only what you requested.

It reduces token usage significantly by returning only the extracted text or attributes. You aren't feeding the entire raw HTML payload into your LLM's context window, which saves money and keeps your prompts clean.

Your HTML data stays within your local execution environment or the Vinkius-managed sandbox during the parse. We don't log your payloads or store the scraped content, ensuring your source data remains private throughout the chain.

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