How to Use the Contentsquare MCP in LangChain

Q: How do I fetch Contentsquare metrics in LangChain?

Use the getmetrics tool within your agent's tool set. It returns the raw performance data your chain needs to make informed decisions.

Q: Can LangChain automate Contentsquare export jobs?

Yes, you can trigger createexportjob directly from your code. This lets your agent manage the entire extraction process autonomously.

Build multi-step UX reasoning chains in LangChain by connecting directly to your Contentsquare data.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Contentsquare MCP to LangChain

Create your Vinkius account to connect Contentsquare 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.

GDPR Free for Subscribers

Setup Contentsquare with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain UX metrics into LangChain pipelines

Feed live engagement numbers straight into your reasoning chain. You can call `get_metrics` to pull bounce rates and convert that raw output into the next step of your agent workflow. This MCP Server allows LangChain agents to process site performance without manual data entry. Your agent decides when to trigger `get_page_metrics` based on previous chain results.

Automate Contentsquare exports via LangChain

Trigger `create_export_job` programmatically during your agent's execution flow. This removes the need for manual dashboard interaction when you need to pull raw data pipelines. LangChain agents handle the lifecycle of these tasks by calling `list_export_jobs` to verify status. You keep your data extraction logic tight within the same chain that analyzes the results.

Map site paths within LangChain

Sync your agent's understanding of site architecture using `list_mappings`. Your LangChain logic can now traverse URL paths dynamically to locate specific zones for analysis. Combine this with `list_zonings` to pinpoint exactly where users interact on a page. The agent acts on structural data rather than guessing URL patterns.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Contentsquare. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Contentsquare now

Real-time monitoring

Live

visibility into every interaction

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

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

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

Common questions about Contentsquare MCP in LangChain

Use the `get_metrics` tool within your agent's tool set. It returns the raw performance data your chain needs to make informed decisions.

Yes, you can trigger `create_export_job` directly from your code. This lets your agent manage the entire extraction process autonomously.

Absolutely. You can pass the full tool list to your agent, allowing it to pick the right function for site mapping or data extraction.

Vinkius manages the auth layer. You simply provide the endpoint token, and the MCP Server handles the secure handshake.

Your analytics data stays within your controlled environment. We only handle the raw session logs you specifically request via the tools.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

Start using the Contentsquare MCP today

We host it, we monitor it, we maintain it. You just paste one token.

Get started