Driving agentic innovation w/ MCP as the backbone of tool-aware AI
summary
Microsoft describes the Model Context Protocol (MCP) as a standardized, secure connector letting LLM-driven agents call tools like GitHub, Playwright, Figma and Azure services; demos show a markdown-driven job that reads GitHub via a GitHub MCP server, runs market research, generates HTML and tests it with Playwright, all deployed as an Azure Functions MCP server and published to a private catalog via Azure API Center; architecture contrasts local vs remote MCP servers and three registries (open Anthropic, enterprise, on-device), and Foundry already exposes 1,000+ Foundry tools.
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chapters (13)
- 0:00Welcome & Introduction: Building Tool-Aware Agents with MCP
- 0:24What is an Agent? Deterministic vs. Non-Deterministic Programs
- 1:23Why Tools Matter: Retrieval, Actions, and Memory in AI Agents
- 2:09Enter MCP: Standardizing Tool Access Across Ecosystems
- 3:21MCP Explained: Secure, Unified Access for Agents and Assistants
- 4:02MCP Clients and Servers: How They Work Together
- 5:00Types of MCP Servers: Local vs. Remote and Their Use Cases
- 6:10Demo: Using MCP Servers for Automated Reporting and Testing
- 8:16Microsoft’s Bet on MCP: First-Party Servers and Integration Options
- 9:56Foundry Tools: Access 1,000+ Tools for Building Agents
- 10:44Managing MCP Servers: Registries and Enterprise Governance
- 13:16Demo: Deploying MCP Servers with Azure API Center
- 15:22Wrap-Up: From Code to Registry and Building Enterprise-Ready Agents
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