Contentsquare MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Contentsquare through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Contentsquare "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Contentsquare?"
)
print(result.data)
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.
Pydantic AI validates every Contentsquare tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Contentsquare MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the Contentsquare MCP Server
Pydantic AI provides unique advantages when paired with Contentsquare through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Contentsquare integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Contentsquare connection logic from agent behavior for testable, maintainable code
Contentsquare + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Contentsquare MCP Server delivers measurable value.
Type-safe data pipelines: query Contentsquare with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Contentsquare tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Contentsquare and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Contentsquare responses and write comprehensive agent tests
Contentsquare MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Contentsquare to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Contentsquare to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiContentsquare + Pydantic AI FAQ
Common questions about integrating Contentsquare MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.