Contentsquare MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Contentsquare as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Contentsquare. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Contentsquare?"
)
print(response)
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.
LlamaIndex agents combine Contentsquare tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Contentsquare MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Contentsquare
Why Use LlamaIndex with the Contentsquare MCP Server
LlamaIndex provides unique advantages when paired with Contentsquare through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Contentsquare tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Contentsquare tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Contentsquare, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Contentsquare tools were called, what data was returned, and how it influenced the final answer
Contentsquare + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Contentsquare MCP Server delivers measurable value.
Hybrid search: combine Contentsquare real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Contentsquare to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Contentsquare for fresh data
Analytical workflows: chain Contentsquare queries with LlamaIndex's data connectors to build multi-source analytical reports
Contentsquare MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Contentsquare to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Contentsquare to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpContentsquare + LlamaIndex FAQ
Common questions about integrating Contentsquare MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.