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MCP Apps with Claude Code: Build an Interactive Analytics Dashboard

MCP Apps with Claude Code: Build an Interactive Analytics Dashboard

1h 9mIntermediate2026-06-11

Authors

Lillian Pierson, P.E.

Lillian Pierson, P.E.

Engineer, CEO, and Head of Product at Data-Mania

Course details

MCP Apps add graphical interfaces to the connective power of the Model Context Protocol (MCP). In this course, author and data science leader Lillian Pierson teaches you how to build an MCP App that renders an interactive analytics dashboard inside a Claude Code MCP host while enforcing enterprise-style governance patterns. Rather than focusing on generic chat-to-SQL workflows, Lillian shows you how to architect a controlled AI layer in front of business data systems. By the end of this course, you’ll have created a functioning MCP server, a UI resource that renders inside the MCP host, an interactive analytics dashboard, role-based data access enforcement, and an audit log of tool usage. The dashboard analyzes simulated enterprise data provided as CSV files (CRM, billing, product usage, and support), representing exports from real-world business systems.

Learning objectives
Design approved, schema-based analytics tools that expose metrics instead of raw queries, aligned with enterprise governance patterns.
Implement role-aware permissions that control which metrics, filters, and drill-downs users can access.
Render a true MCP App UI with metric selection, filters, and consistent chart outputs driven by tool results.
Enable interactive drill-downs while logging every tool invocation for transparency, traceability, and compliance.

Concepts

Introduction

  • Build a dashboard with MCP Apps and Claude Code
  • Explore MCP Apps and our course build

Project Setup and Data

  • Set up the MCP App project
  • Review the enterprise CSV data
  • Define roles and approved metrics
  • Explore the two-layer architecture

Build the MCP Server Tools

  • Create the first tool - List metrics
  • Build the Python data API
  • Run metric queries against the dataset
  • Add audit logging for tool calls

Build the MCP App UI

  • Return a UI resource from the MCP server
  • Add role and metric controls
  • Render the first chart

Make the App Interactive

  • Add drill-down analysis
  • Test and debug the app end-to-end
  • Deploy to Claude

Conclusion

  • Next steps and extensions

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