Contentsquare MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Contentsquare through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"contentsquare": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Contentsquare, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Contentsquare MCP Server
Connect your Contentsquare account to any AI agent and take full control of your digital experience analytics and UX monitoring through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Contentsquare through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Project & Metric Auditing — List project directories and retrieve explicit site metrics including bounce rates, engagement, and conversion telemetry
- Audience Segmentation — Access standard API demographic directories to classify user behaviors and validate platform segments globally
- URL & Zoning Analysis — Discover explicit routing trees for URL paths and inspect deep interaction arrays like heatmap coordinates and button zones
- Raw Data Exports — Trigger automated raw data pipeline extractions for sessions or pageviews to feed your external BI tools or data science workflows
- Session Enrichment — Mutate global boundaries by appending offline attributes (like sales or contact logs) to live active interaction blocks
- Page-Level Deep Dives — Execute direct queries for specific document nodes to track detailed behavioral limits against exact page URLs
The Contentsquare MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Contentsquare to LangChain via MCP
Follow these steps to integrate the Contentsquare MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Contentsquare via MCP
Why Use LangChain with the Contentsquare MCP Server
LangChain provides unique advantages when paired with Contentsquare through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Contentsquare MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Contentsquare queries for multi-turn workflows
Contentsquare + LangChain Use Cases
Practical scenarios where LangChain combined with the Contentsquare MCP Server delivers measurable value.
RAG with live data: combine Contentsquare tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Contentsquare, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Contentsquare tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Contentsquare tool call, measure latency, and optimize your agent's performance
Contentsquare MCP Tools for LangChain (10)
These 10 tools become available when you connect Contentsquare to LangChain via MCP:
create_export_job
Dispatch an automated validation check routing Raw Data Pipeline chunks
enrich_session
g. Sales, Contact logs) binding native JSON payloads executing directly towards session arrays. Mutate global Web CRM boundaries appending headless Offline attributes to live sessions
get_export_job
Validate Data Science object extraction execution state queues
get_metrics
Retrieve explicit UX logging tracing explicit bounce / engagement metrics
get_page_metrics
Execute static generation targeting exactly formatted URL statistical bodies
list_export_jobs
Perform structural log extraction matching asynchronous Raw export payloads
list_mappings
Discover explicit routing trees structuring specific URL paths
list_projects
Identify bounded UX tracking domains inside the Headless Contentsquare platform
list_segments
Provision highly-available JSON arrays holding demographic limits
list_zonings
Inspect deep internal interaction arrays mitigating specific Click tracking constraints
Example Prompts for Contentsquare in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Contentsquare immediately.
"List all active projects in Contentsquare"
"Get site metrics for last week"
"Create a raw data export for sessions from yesterday"
Troubleshooting Contentsquare MCP Server with LangChain
Common issues when connecting Contentsquare to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersContentsquare + LangChain FAQ
Common questions about integrating Contentsquare MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Contentsquare with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Contentsquare to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.