Leader’s Guide to Model Context Protocol (MCP)

From splashy product launches, public discord, or boardroom conversations—you’ve no doubt heard talk around MCP. But what do Model Context Protocols really mean for your business’s AI strategy?

As a leader who’s seen the hype cycle before, you know buzzwords aren’t enough. You need real answers, proven use cases, and a clear path to value. That’s why we created this guide.

Inside, you’ll discover:

Tangible ways MCP is solving challenges in enterprise AI systems
How to navigate gaps in accuracy, trust, security, and governance
Where MCP fits in the evolution of analytics—and what’s the next frontier

The bottom line?

MCP isn’t just another tech trend. It’s the next layer of intelligence your organization needs to confidently build smarter AI systems, faster.

ThoughtSpot Agentic Analytics Platform

Analyst Studio

Data Mashups, Data Science,
Python Notebooks, Ad Hoc SQL Analysis

Semantic Model

Trusted & Secure Metrics Layer, Agent-ready
Metadata, Data Security

Data Management

Real time, Zero Copy or In-Memory,
Unified Metadata and Compliance

AI Agents

Spotter - Agentic AI Analyst,
Deep Research, Agent as a Service

AI-Augmented Dashboards

AI-first, Mobile-ready, Automated Insights,
Connected Where You Work

Smart Apps

Embedded Analytics for your customers, Insights to
Actions, APIs and SDKs, Workflow Automation

Smart Apps

Automated
Insights

Data Hub

AI Agents

AI Augmented
Dashboards

Smart Apps

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