How Model Context Protocol (MCP) Turned Scattered Tool Definitions into a Stable, Discoverable Server
English summary
The author details their adoption of Anthropic’s Model Context Protocol (MCP) to replace ad-hoc, scattered tool definitions with a centralized, discoverable server. MCP enables AI agents to dynamically discover and invoke tools, reducing complexity and improving reliability. The shift moved the agent architecture away from fragile, hardcoded integrations toward a stable, protocol-driven approach. This server-based design allows tools to be added or updated without modifying the agent’s core logic.
Chinese summary
作者详述了如何采用 Anthropic 的模型上下文协议 (MCP) 将原先零散、随意的工具定义替换为集中的、可发现的服务器。MCP 使 AI 智能体能够动态发现和调用工具，降低了复杂性并提高了可靠性。这一转变将智能体架构从脆弱的硬编码集成转向稳定、协议驱动的方式。基于服务器的设计允许在不修改智能体核心逻辑的情况下添加或更新工具。
Key points
Adopted Anthropic’s Model Context Protocol (MCP) to standardize tool interactions.
采用 Anthropic 的模型上下文协议 (MCP) 标准化工具交互。
Replaced ad-hoc, scattered tool definitions with a centralized MCP server.
用集中的 MCP 服务器取代了零散的工具定义。
The server-based approach improved stability, discoverability, and maintainability of the agent system.
基于服务器的方法提升了智能体系统的稳定性、可发现性和可维护性。