Microsoft Clarity MCP Server

The Microsoft Clarity Data Export MCP Server bridges the gap between AI assistants and web‑analytics insights by exposing Clarity’s rich dataset through a lightweight, protocol‑friendly interface. Instead of wrestling with raw API calls and authentication headaches, developers can ask their AI partner to pull session metrics, user journeys, or engagement statistics directly into a conversation. This eliminates manual data export steps and allows analysts to iterate on hypotheses in real time.

At its core, the server accepts structured queries that specify up to three dimensions—such as Browser, Device, or Country/Region—and returns a curated set of metrics like Scroll Depth, Engagement Time, and Traffic counts. The result is delivered in a consistent JSON payload that MCP‑compatible clients can interpret, cache, or transform on the fly. For developers building custom dashboards, chat‑based reporting tools, or automated alerts, this means a single, well‑defined endpoint replaces multiple REST calls and reduces latency.

Key capabilities include:

• Dimension filtering: Narrow data to the most relevant slices of your user base without writing complex query strings.
• Metric selection: Pull exactly the analytics you need—whether it’s a high‑level traffic overview or deep dive into session depth.
• Seamless integration: Ready‑to‑use plugins for Visual Studio Code and Claude Desktop mean you can add the server to your workflow with a single click, then reference it in any MCP client configuration.
• Token‑based security: The server accepts a Clarity API token via command line or tool parameters, keeping credentials out of code and ensuring secure, scoped access.

Real‑world scenarios that benefit from this MCP server include:

• Rapid hypothesis testing: A product manager asks the AI to compare engagement time between mobile and desktop users, receiving an instant answer that informs feature prioritization.
• Automated reporting: A BI pipeline triggers the server to fetch weekly traffic metrics, then feeds them into a Tableau dashboard without manual exports.
• Developer support: When troubleshooting a performance issue, an engineer can ask the AI for device‑level click heatmaps to pinpoint problematic UI elements.

Integrating the server into existing AI workflows is straightforward: configure your MCP client with a single command that points to the executable and supplies the API token. Once connected, any tool that supports MCP can call the operation, passing desired dimensions and metrics. The server handles authentication, query translation, and response formatting, letting the AI focus on analysis rather than plumbing.

What sets this MCP server apart is its focus on usability and security. By bundling a pre‑configured client plugin, developers can avoid the typical friction of installing and maintaining an external service. Moreover, the ability to filter by multiple dimensions in a single request reduces the need for post‑processing, making AI‑driven insights faster and more accurate. For anyone who relies on Microsoft Clarity for user experience data, this MCP server transforms raw analytics into conversational intelligence that can be leveraged across teams and tools.