A ubiquitous public account for pirate enthusiasts, regularly sharing curated experiences, tech learning, and growth insights. Currently publishing articles on AI RAG customer service, AI MCP technology, and open-source design. Personal free Knowledge Planet: Awakening New World Programmer.
This guide walks through creating a stateless Streamable HTTP MCP Server using the TypeScript SDK, explaining the protocol’s shift from HTTP + SSE to Streamable HTTP, detailing setup, code structure, endpoint handling, session management, and deployment, and highlighting performance and stability advantages for AI applications.
This tutorial walks through creating a weather‑focused MCP server with the Model Context Protocol TypeScript SDK, explaining Server‑Sent Events, setting up the Node.js environment, writing the TypeScript code, building, running, and testing the HTTP SSE service end‑to‑end.
This article explains the motivation behind Model Context Protocol (MCP), compares its communication modes, and provides a step‑by‑step tutorial for creating a TypeScript‑based MCP Server in Stdio mode that exposes a weather‑lookup tool, builds it, runs it, and tests it with the official inspector.
This article walks through creating a TypeScript‑based train‑ticket MCP Server that leverages the Qwen‑3 14B model and Model Context Protocol, detailing setup, configuration, the provided query and time tools, example interactions, and links to the open‑source repositories.
This article explains the Model Context Protocol (MCP), why it simplifies AI tool integration, and provides a step‑by‑step guide to create a Spring AI 1.0.0‑M7 Maven project, configure the MCP server with HTTP SSE, implement weather and Maoyan movie services, run the server, and test it with the MCP Inspector and Cherry Studio client.
The article explains how large language models extend their capabilities by using tool calls—detailing the need for external APIs, the three‑stage workflow, request/response formats, autonomous routing, parameter generation, error handling, and the relationship between Function Calling and Tool Use.
The article walks through creating a Retrieval‑Augmented Generation (RAG) based AI chatbot for Tongcheng Travel by preparing FAQ data, formatting it with L1/L2/Q/A tags, importing it into FastGPT knowledge bases, configuring a workflow with intent classification and prompt engineering, and validating the system with multiple test cases.
This tutorial walks through setting up a Node.js environment, creating a TypeScript project, writing a custom OpenAI client, and integrating either Zhipu AI or an Ollama‑deployed model to perform automated code reviews via GitLab CI, complete with build, run, and VS Code debugging steps.
This tutorial walks through privately deploying the DeepSeek R1 14B model on an AutoDL GPU server using Ollama, configuring systemd and ports, exposing the service, and integrating it with a GitLab CI pipeline to generate AI‑driven code review results sent via a Feishu bot.
This tutorial walks through upgrading Git on a GitLab server, using the Commit API to fetch recent changes, invoking an AI model for automated code review, posting the review as a commit comment, and notifying the team via a Feishu custom‑bot webhook.
