Intro

Recently, Google released the A2A protocol, extending the Model Context Protocol (MCP) proposed by Anthropic last year. Before diving into A2A, I wanted to take some time to document my notes on MCP.

📌 This article focuses on more advanced MCP topics, including MCP Agents, Sampling, Composability, Registry, and future developments.

Friendly reminder: this article turned out longer than expected, so it is split into two parts. Feel free to jump to the sections you are most interested in 🥸

MCP Agents and System Architecture

An MCP Agent is a powerful framework that provides the following core capabilities:

A simple and flexible context injection mechanism
Declarative frameworks for implementing various workflows
A rich set of building blocks for constructing agents

Taking LastMile AI’s MCP Agent as an example, it provides a lightweight framework for building agents using MCP 🤖

https://github.com/lastmile-ai/mcp-agent

System Architecture Considerations

Component	Responsibilities
Client	- Does not need to handle retry logic- Does not need to manage logging details
Server	- Closer to the final application- Has greater control over system-level interactions

Scalability and Limitations

Current model capacity constraints:

Standard models (e.g., Claude): ~50–100 tools
Advanced models: several hundred tools

Tool Management Strategies

To manage large toolsets effectively, the following approaches may be useful: