Contentsquare MCP. Audit site metrics and map user behavior via chat.
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Contentsquare MCP Server manages UX analytics by letting your AI agent read and act on your site's performance data. You can list projects, check bounce rates, get demographic segments, and even trigger raw data exports.
It turns complex Contentsquare reports into simple chat commands, making site auditing and behavioral analysis immediately actionable for your workflow.
What your AI agents can do
Create export job
Starts an automated process to validate and route chunks of raw data pipelines.
Enrich session
Attaches external data—like sales or contact logs—to existing user session records.
Get export job
Checks the current status of a data science object extraction job.
The agent retrieves explicit site metrics, including bounce rates, engagement, and conversion telemetry, for a given project.
The agent accesses and lists standard API demographic data to classify user groups and validate market segments.
The agent discovers the routing structure for specific URL paths and inspects deep interaction arrays like heatmap coordinates.
The agent initiates an automated job to extract raw data pipelines for sessions or pageviews, making it available for external BI tools.
The agent modifies global data boundaries by adding offline attributes (like sales or contact logs) to existing live user sessions.
The agent executes targeted queries for specific page URLs to track detailed user behavior against that exact document node.
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Contentsquare MCP Server: 10 Tools for UX Analytics
These tools allow your agent to read, map, enrich, and export every type of data point from your digital experience, making deep analytics accessible via conversation.
019d757acreate export job
Starts an automated process to validate and route chunks of raw data pipelines.
019d757aenrich session
Attaches external data—like sales or contact logs—to existing user session records.
019d757aget export job
Checks the current status of a data science object extraction job.
019d757aget metrics
Retrieves specific user experience metrics, such as bounce and engagement rates.
019d757aget page metrics
Runs a static query to get statistical data for a precisely formatted URL page.
019d757alist export jobs
Lists all active and completed raw data export jobs.
019d757alist mappings
Discovers the full routing tree structure for specific URL paths.
019d757alist projects
Identifies all bounded UX tracking domains within the Contentsquare platform.
019d757alist segments
Gets a list of available JSON arrays containing demographic user data.
019d757alist zonings
Inspects detailed internal interaction data, such as specific click tracking constraints.
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What you can do with this MCP connector
Your AI agent connects to the Contentsquare MCP Server to handle UX analytics and site performance data. You'll use this to audit site performance metrics, identify user segments, map website URLs, export raw data, append offline data to sessions, and query specific page behavior.
Audit Site Performance Metrics: Your agent uses get_metrics to pull specific user experience metrics like bounce and engagement rates for a project, and it runs get_page_metrics for statistical data on a precise URL. You can also call list_projects to see all the bounded UX tracking domains available in Contentsquare.
Identify User Segments: To classify user groups and validate market segments, your agent uses list_segments to get a list of available JSON arrays containing demographic user data. It also calls list_mappings to discover the full routing tree structure for specific URL paths.
Map Website URLs: You can run list_zonings to inspect detailed internal interaction data, like specific click tracking constraints. The agent uses list_mappings to discover the full routing tree structure for specific URL paths.
Export Raw Data: To get raw data pipelines for sessions or pageviews, your agent first calls list_export_jobs to see what jobs are running or finished, and it uses list_export_jobs to list all active and completed raw data export jobs. It kicks off the extraction process using create_export_job to validate and route chunks of raw data pipelines.
You can check the status of these jobs with get_export_job.
Append Offline Data to Sessions: Your agent modifies global data boundaries by using enrich_session to attach external data—like sales or contact logs—to existing user session records.
Query Specific Page Behavior: For detailed user behavior, your agent runs a targeted query for specific page URLs using get_page_metrics. You can also call list_zonings to inspect detailed internal interaction data, like specific click tracking constraints.
How Contentsquare MCP Works
- 1 First, subscribe to the Contentsquare server and provide your Contentsquare Client ID, Client Secret, and Project ID (you find these in the Contentsquare Console under API Credentials).
- 2 Next, tell your AI client exactly what you want to audit. For example: 'What was the bounce rate for the main checkout page last week?'
- 3 The agent runs the necessary tool calls, pulls the data, and delivers the metrics or insights directly in the chat conversation.
The bottom line is, you talk to your AI agent like you're talking to a data analyst, and it runs the complex queries for you.
Who Is Contentsquare MCP For?
This is for the Product Manager who needs to track engagement trends without diving into dashboards. It’s for the Data Analyst who needs to trigger raw data exports and enrich sessions directly from the chat. Use it if your job involves translating complex UX metrics (like heatmap zones or conversion rates) into immediate, conversational insights.
Audits user behavior metrics and heatmap zonings for specific areas of the site without navigating the full Contentsquare console.
Monitors project-wide engagement and conversion trends using natural language queries in the chat.
Triggers raw data exports and enriches user sessions with offline data directly from the chat interface.
Verifies audience segment behavior and audits page-level performance for specific marketing campaigns.
What Changes When You Connect
- Stop digging through dashboards. Use
get_metricsto get site engagement, bounce rates, and conversion telemetry immediately. No more clicking through multiple tabs to find a single number. - Enrich data on the fly. Instead of exporting a session log and manually joining it with a CRM, run
enrich_sessionto append sales or contact logs directly to the active user data. - Understand the user path.
list_mappingsreveals the entire routing tree for your URLs, letting you know exactly how users get from page A to page Z without manual link auditing. - Isolate the problem area. Use
get_page_metricsto focus your audit. You can run queries against a specific document node and see exactly what happened on that single page. - Process massive data sets. When you need raw data for Tableau or Snowflake, use
create_export_jobandlist_export_jobsto kick off and track large-scale data exports. - Classify your audience instantly. Run
list_segmentsto pull available demographic data, allowing you to validate user behavior against predefined groups.
Real-World Use Cases
Validating a New Funnel Step
The Product Manager notices conversion rates dropped after the payment button. Instead of manually checking the full console, they ask their agent: 'What are the metrics for the checkout page?' The agent uses get_page_metrics and returns the bounce rate, engagement score, and conversion telemetry for that exact URL, letting them pinpoint the failure immediately.
Investigating Segment Performance
The Marketing team wants to know if high-value users (the 'Premium' segment) are behaving differently than general users. They prompt the agent to list segments and then run a comparison query. The agent uses list_segments and then cross-references the data to validate if the Premium group has lower bounce rates, proving the segment's value.
Auditing a Complex Site Structure
The UX Researcher suspects a flaw in the site's routing logic. They ask the agent to map the URLs. The agent runs list_mappings and provides the full routing tree, showing exactly which paths are accessible and where the expected link flow is broken.
Feeding BI Tools with Live Data
The Data Analyst needs to run a custom report in Snowflake, but the data is trapped in Contentsquare. They ask the agent to export the raw data for the last 24 hours. The agent uses create_export_job and provides a job ID, which the analyst can then use to track the data ingestion via get_export_job.
The Tradeoffs
Treating data like static reports
Trying to get a holistic view of user behavior by running 10 separate, manual queries in the Contentsquare UI, then copying and pasting the numbers into a spreadsheet.
→
Instead, let your agent use get_metrics for general stats, and then use list_zonings to pull deep interaction arrays (like heatmap coordinates) for specific zones, combining the results in a single chat output.
Ignoring the data lifecycle
Analyzing conversion rates (get_metrics) based on a session log that hasn't been updated with post-purchase sales data.
→
Always run enrich_session first. This attaches offline attributes (like the final sale amount) to the session record, ensuring your metrics are complete before you analyze them.
Over-relying on basic listing tools
Just running list_projects and assuming that tells you everything about the site's performance or structure.
→
After listing projects, always follow up by using list_mappings to understand the structural boundaries, and then use get_metrics to see the actual performance inside those boundaries.
When It Fits, When It Doesn't
Use this server if you need to programmatically audit user behavior, map site structure, or export raw data for external analysis. This is perfect for data teams and product managers who need to execute multi-step analytics pipelines (e.g., list_segments -> list_zonings -> get_metrics).
Don't use this if you just need a simple definition or a conceptual overview of a feature. If you only need to know 'what is a bounce rate?', just read the documentation. If you need to calculate the bounce rate for a specific segment, use get_metrics.
If your goal is to connect disparate data sources (like combining web activity with CRM sales data), then enrich_session is the key tool, and you must use it in conjunction with the other tools.
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.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual UX Audits Are Slow.
Right now, auditing a user flow means jumping between the Contentsquare console, exporting data, and running separate queries for metrics, demographics, and page-specific deep dives. You spend hours copy-pasting data and stitching together fragmented reports just to understand why conversion dropped.
With the Contentsquare MCP Server, you just talk to your agent. You ask: 'Show me the bounce rate and segment breakdown for the checkout page.' The agent runs `get_metrics`, uses `list_segments`, and delivers a single, cohesive report instantly.
Contentsquare MCP Server: Audit Data Lineage
The old way required you to separately check project boundaries (`list_projects`), then map the paths (`list_mappings`), and finally export the raw data (`create_export_job`). This process was linear, slow, and required multiple manual steps.
Now, you can run a complex audit in one go. The agent coordinates the necessary calls, letting you check the routing structure and initiate the data pipeline simultaneously. You get the full data lifecycle, managed by conversation.
Common Questions About Contentsquare MCP
How do I use the `get_metrics` tool with Contentsquare MCP Server? +
You tell your agent what specific metric you need and for what time frame. The agent then runs get_metrics and returns the explicit data points like bounce rate or engagement percentage in the chat.
Can I use `enrich_session` to combine web data with sales data? +
Yes. The enrich_session tool lets you mutate global data boundaries by appending offline attributes (like sales or contact logs) to live session arrays, giving you a complete picture.
What does `list_mappings` do for URL analysis? +
list_mappings discovers the explicit routing tree for your site's URLs. It shows you all possible paths and how they are structured, which is critical for understanding user flow.
How do I check if a raw data export job started with `create_export_job` is finished? +
You use get_export_job. This tool validates the status of the data science object extraction job and tells you if the raw data chunks are ready for download or review.
Is `list_segments` the same as `list_projects`? +
No. list_projects finds the main UX tracking domains (like 'Main Website'). list_segments provides the actual JSON arrays holding the demographic data used to classify users within those projects.
What do I need to use the `list_projects` tool to find my tracking domains? +
You must provide your Contentsquare Client ID, Client Secret, and Project ID. Once authenticated, the list_projects tool returns a list of all bounded UX tracking domains within your Contentsquare account.
How does `get_page_metrics` handle specific URL paths? +
The get_page_metrics tool targets exact URL paths. You simply pass the specific document node or URL to the tool, and it returns detailed behavioral limits for that exact page.
What is the purpose of the `list_zonings` tool? +
The list_zonings tool inspects deep interaction arrays. It helps you identify specific click tracking constraints and analyze button zones across your site.
Can my agent export raw session data from Contentsquare? +
Yes. Use the 'create_export_job' tool with your desired date range and data type (sessions or pageviews). The agent will initiate the asynchronous extraction and provide you with a job ID to monitor the download status.
How do I enrich an active session with offline sales data? +
Provide the session ID and a JSON object containing the properties you want to append. The 'enrich_session' tool will push these attributes directly to the active interaction block within the Contentsquare engine.
Can I check the engagement metrics for a specific landing page URL? +
Absolutely. Use the 'get_page_metrics' tool. Provide the exact page URL and a date range. Your agent will return detailed statistical bodies including bounce rates and behavioral limits for that specific web document.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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