MCP and the future of open AI: how Contentsquare is bridging the gap between agents and experience data

If you haven’t heard of it yet, MCP is an emerging open standard that allows AI agents to communicate with one another, securely sharing context and capabilities. Think of it as a universal translator for AI—a way for tools like Jira, Optimizely, or Contentsquare to expose their functions in a common language that other AI systems can understand and act upon.

In our demo, we showed how Claude, Anthropic’s conversational AI, can connect directly to Contentsquare via MCP. A user could simply ask, “Which parts of our checkout journey are causing drop-off this week?” and Claude would fetch and summarize the relevant insights directly from Contentsquare data.

No logins. No dashboards. Just answers—instantly.

And this is only the beginning. We’ve already tested and demonstrated our integration with Claude, and we’re now extending support for ChatGPT, with plans to follow for Gemini, Microsoft Copilot, and more—making Contentsquare insights available wherever teams collaborate and make decisions.

1. Querying Contentsquare directly from your AI assistant

MCP opens the door for anyone, from executives to product managers, to query Contentsquare directly from their preferred LLM, whether that’s ChatGPT Enterprise, Claude, or Gemini.

Instead of logging into the platform or navigating dashboards, users can simply ask:

• “What’s our mobile conversion rate this week?”

• “Which pages are causing the highest frustration on our app?”

Behind the scenes, the LLM uses the Contentsquare MCP server to pull real behavioral insights and give you natural-language answers. It’s the simplest form of contextual access: data where you work, not another tab you need to open.

2. Combining insights across other MCP-enabled tools

Where MCP really shines is in multi-system queries—when your AI assistant can combine insights from several platforms at once.

Imagine this workflow:

• You ask your AI agent, “What’s the status of the new feature rollout, and how is it impacting user engagement?”

• The agent queries Jira (via its MCP interface) to check release status and sprint updates

• Then it calls the Contentsquare MCP server to see if user journeys or conversion rates changed after the rollout

• It may even query your ad platform’s MCP endpoint to pull campaign data, correlating marketing activation with on-site impact

In seconds, you have a unified story, from launch to experience outcome, no manual data stitching required.

This is where MCP turns AI assistants from reactive chatbots into connected business copilots capable of synthesizing data across systems and revealing cause and effect in real time.

3. Autonomous agents that orchestrate analysis over time

Now imagine the same scenario, but happening automatically. Your AI agent doesn’t wait for you to ask; it’s already configured to run cross-platform checks on a schedule, pulling from MCP-enabled systems to track KPIs across the business.

Every Monday morning, your agent could:

• Query Jira for feature deployments

• Fetch conversion and UX metrics from Contentsquare

• Pull campaign data from an ad platform

Then generate a summary like: “Feature X launched last Thursday. Traffic from the new campaign increased 22%, but mobile checkout friction rose by 8%. Recommend reviewing the payment form UX.”

That’s the power of MCP—a world where AI doesn’t just analyze data, but connects and reasons across it, automatically delivering insights that once required multiple teams and tools.

Getting started with MCP and Contentsquare

The beauty of MCP is that it’s designed for openness—it allows any compliant AI agent to securely connect to platforms that expose an MCP ‘server.’ Contentsquare’s MCP server acts as that gateway, exposing analytical capabilities through a standardized API that can be accessed by enterprise AI tools such as Claude, ChatGPT, Gemini, and internal LLMs.

Here’s how teams can get started today:

1. Join the early access program

Contentsquare’s MCP integration is currently available through an early access program.

To request access, contact your Contentsquare Customer Success Manager or Solutions Architect to be added to the early rollout group.

2. Connect your preferred LLM

Once access is granted:

1. Create a connection: paste a single link into your AI tool’s settings

2. Allow access to Contentsquare: agree to let your AI tool access your CSQ data

Start asking questions like: “Using the Contentsquare MCP connector, summarize the key friction points in our checkout flow last week” and start getting CSQ insights directly in your AI tool 

3. Extend across your ecosystem

After the initial setup, organizations can integrate Contentsquare into their broader MCP network, connecting it to:

• Jira for release correlation

• Optimizely or A/B Tasty for experiment performance

• CRM systems or ad platforms for campaign impact

• Internal data lakes for enrichment and governance

Your AI agent can now reason across all of them, for instance:

“Compare post-launch conversion on the new checkout flow (from Jira release 425) with traffic from the latest campaign (from Optimizely) using Contentsquare data.”

4. Automate through AI agents

For advanced teams, MCP enables scheduled or autonomous analysis. Your internal AI agent can run MCP queries on a defined cadence (daily, weekly, or event-triggered), combining multiple data sources to produce automatic business summaries or anomaly alerts.

For example, “Every Friday, generate a cross-platform summary combining Jira, Contentsquare, and ad data, and post the results directly into Slack or Teams for review.”

5. Build, expand, and customize

You can connect the MCP server to your agent, and then customize the agent. For example, 

• You can create an agent with a tailored prompt that guides how it uses tools, like it can get good at performing Journey Analysis 

• The MCP server can be integrated into agents built with Microsoft Copilot, which can then be connected to platforms like Teams

• The MCP server can also be used in platforms that support building agentic workflows

The bigger picture

For our most advanced customers who are already building internal AI ecosystems, Contentsquare’s MCP server represents a foundational building block. It’s powered by the same technology behind Sense Analyst, giving your AI agents structured, behavioral intelligence that goes far beyond basic metrics, enabling reasoning, automation, and decision-making at scale.

As organizations evolve their AI strategies, one thing is clear: the future isn’t a single agent—it’s a network of them. MCP is how they’ll speak to each other, and with Contentsquare, your experience data becomes the connective tissue between business actions and user impact.