#MCP server Python
278 related articles
·3 min
Adversarial Testing of AI Agents in the Terminal via MCP
Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.
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·2 min
Spring AI in Action: A Complete Guide to LLM Integration for Java Developers
Spring AI is Java's answer to LangChain — offering unified multi-model APIs, structured output, RAG, Tool Calling, and MCP protocol support for enterprise LLM development.
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·3 min
The Complete OpenAI Codex Practical Guide: From Installation to Enterprise Deployment
A complete guide to OpenAI Codex: CLI setup, slash commands, AGENTS.md, MCP integration, multi-agent collaboration, and a RAG customer service project walkthrough.
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·3 min
LangChain4j Getting Started Guide: A Java Engineer's Fast Track to AI Application Development
LangChain4j is the AI application development framework built for Java engineers. Integrate DeepSeek, Qwen, and more into Spring Boot — no Python required.
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·2 min
Complete Spring AI Tutorial: ChatModel, RAG, and MCP Practical Guide
A complete Spring AI guide for Java developers covering ChatModel, EmbeddingModel, ChatMemory, Tool Calling, MCP protocol, and RAG with Milvus. Build LLM apps in Spring Boot.
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·3 min
Harness Architecture in Practice: A Deep Dive into Enterprise-Level Agent Projects
A deep dive into Harness architecture in enterprise Agent projects, covering MCP protocol, sandbox isolation, multi-model scheduling, and ASGI deployment — key topics for LLM job interviews.
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·4 min
LangChain V1.3 in Practice: Master AI Engineering Thinking and Break Free from Tool Dependency
LangChain V1.3 course deep-dive: why engineering thinking beats tool-chasing. Covers RAG accuracy myths, Token cost control, and LangChain/LangGraph/Deep Agent breakdowns.
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·3 min
LangChain V1.3 Hands-On Tutorial: A Complete Breakdown of RAG, Multi-Agents, and Engineering Mindset
An in-depth look at LangChain V1.3's core philosophy: from RAG to multi-agent workflows. Master LangGraph, Chain, and DeepAgent, learn token control and Human-in-the-loop, and become a true master of AI app development.
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·3 min
Empowering AI Agents with Skills to Automate WeChat Official Account Analytics
Learn how Skill-powered AI Agents can automatically log in to WeChat Official Account backends, scrape data, sort it, and generate Excel reports — eliminating manual analytics work.
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·3 min
Langchain.js Agent Development: A Guide to Deconstructing the OpenClaw Engine Architecture
An in-depth analysis of AI agent development based on Langchain.js—comparing workflow agents and Agent Loops, deconstructing the TypeScript implementation path of an OpenClaw-like engine, covering structured output, MCP, and LangGraph.
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·6 min
Hands-On With Hermes Agent's Eight Major Updates: A Complete Guide to AI Automation and Self-Evolution
The Hermes Agent gets a major upgrade with eight new features: native iMessage, parallel background sub-agents, Unreal Engine MCP support, a self-evolving Skill Hub, and more. A hands-on breakdown of the core changes and their real impact on personal AI automation workflows.
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·3 min
GPT-5.6 Sol in Action: A 3D Interactive London Website Generated in 5 Minutes
A Reddit user tested GPT-5.6 Sol with a single prompt. In the Row-Bot framework, the model built a 3D interactive London website in 5 minutes—complete with browser verification and vision analysis. A deep dive into AI coding Agents.
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·4 min
TigrimOSR: An Open-Source Agent Framework in Rust with Configurable Agent Loops
TigrimOSR is an open-source multi-agent system written in Rust, supporting full agent loop definition via YAML config files with only 250MB memory usage. A deep dive into Loop Engineering, Rust advantages, and self-hosted Agentic AI.
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·5 min
Browser Use: A Deep Dive into the Event-Driven Browser AI Agent Framework
Browser Use is an open-source AI Agent framework that lets LLMs autonomously drive browser operations via natural language. This article breaks down its four-layer architecture, core Agent loop, CDP perception layer, dynamic Tools dispatch, and its Skills, Sandbox, and MCP extension capabilities.
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·6 min
The Eight-Step Method for Agent Development: A Practical Path from Pitfalls to Production
Many enterprises fail at AI Agents due to choosing the wrong tools and lacking methodology. This article outlines an eight-step Agent development method—from cognitive foundations, scenario selection, hand-writing ReAct, and structured output to Tool Use, RAG, evaluation sets, and production fallback.
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·3 min
Google Open-Sources Stitch Skills: A New Practice in AI Agent Skill Standardization
Google Labs open-sourced stitch-skills, a standardized skill encapsulation solution for AI Agents, compatible with Gemini CLI, Claude Code, Cursor and more.
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·7 min
AI Agent Runs a Fully Automated Chess Channel: A Real-World Practice of Combining LLMs with Engines
A German engineer built a fully automated chess YouTube channel with an AI Agent, combining LLMs and chess engines to auto-generate explainer videos nightly, reaching 500K views. Here's the tech architecture, tool design, and real costs.
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·5 min
AI Agent Custom Tool Development Guide: Boost Efficiency by One-Click Reuse of Repetitive Tasks
An in-depth guide on developing Custom Tools for AI agents, compressing repetitive tasks like Excel-to-Markdown conversion from 30 minutes to under 5 seconds. Covers AGENTS.md registration, tool directory setup, and AI-assisted development.
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·6 min
AI Agent Development in Go: A Hands-On Guide to ByteDance's Eino Framework and Its Seven Core Capabilities
Build production-grade AI Agents with a pure Go stack using ByteDance's Eino framework. A deep dive into seven core capabilities: multi-Agent orchestration, long-task execution, command approval, RAG, MCP, Skills, and database reporting.
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·3 min
GitHub Daily · July 12: The Explosion of the Agent Skills Ecosystem and the Rust Rewrite Wave
July 12 GitHub trending: Agent Skills/MCP ecosystem explodes with superpowers hitting ~900 stars, pgrust rewrites Postgres in Rust passing 100% tests, plus solid engineering foundations.
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