If you’ve already used function calling but keep writing glue code for every non-trivial task, you’re likely at the point where Responses API + MCP makes more sense. This guide is practical: how to move from single tool calls to a scalable agent workflow where retrieval, execution, validation, and write-back follow a consistent structure. ...
如果你已经做过函数调用（function calling），但一上复杂流程就开始写一堆胶水代码，那你差不多到了该用 Responses API + MCP 的阶段。 这篇不讲空概念，直接给你一个可落地的路线：把“模型调用工具”升级成“可扩展 Agent 工作流”，让检索、执行、校验、回写变成标准流程。 ...
如果你把 AI 只当“聊天工具”，三家看起来差不多；但一旦进入真实开发链路，差异会非常明显。 我的结论先放前面：日常编码+项目内改动优先 Codex，长文推理和方案拆解用 Claude，OpenAI CLI 适合做标准化自动化和跨工具串联。 ...
If you use AI as a chatbot only, these tools feel similar. In real engineering workflows, they behave very differently. My conclusion first: use Codex for repo-native coding changes, Claude for deep reasoning and long-form planning, and OpenAI CLI for standardized automation pipelines. ...