chapter eight

8 Standardizing AI Tools using MCP (Model Context Protocol)

 

This chapter covers

  • Understanding Model Context Protocol (MCP) architecture
  • Exploring the transport types: STDIO, HTTP, and Stream
  • Building MCP servers that expose Agent Framework AI tools, prompts and resources
  • Creating MCP clients that consume external tools, prompts and resources

Remember Robby's garage from earlier chapters, filled with specialized tools, each requiring its own adapter? Now imagine if Robby could access an entire warehouse of robotic tools maintained by other developers, all through the same universal MCP interface.

Agent Framework solves this locally using AI tools, but what happens when you want to share those same capabilities across different AI systems?

Model Context Protocol (MCP), was introduced by Anthropic in late 2024. Think of MCP as decoupled and standardized universal adapter for AI applications for consuming tools.

8.1 Introduction to MCP

Just as HTTP standardized how web browsers communicate with servers, MCP standardizes how AI applications discover and use external tools. The protocol creates a three-part ecosystem where each component plays a specific role in enabling cross-platform AI functionality.

8.1.1 MCP Architecture

8.2 Building MCP Tools

8.3 Implementing MCP Server

8.3.1 Transport Types

8.3.2 Building MCP Server

8.3.3 MCP Server with STDIO in Action

8.3.4 MCP Server with HTTP in Action

8.3.5 Third-Party MCP Server Discovery

8.3.6 MCP Inspector

8.4 Implementing MCP Client

8.4.1 Client Transport Mechanisms

8.4.2 Building MCP Client

8.4.3 MCP Client with STDIO in Action

8.4.4 MCP Client with HTTP in Action

8.5 Benefits

8.5.1 MCP Versus Local AI Tools

8.6 Summary