#MCP server Python
275 related articles
·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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·4 min
Microsoft Dataverse Plugin for Coding Agents: Natural Language-Driven Enterprise Low-Code Development
Microsoft Power Platform's Dataverse plugin for coding agents supports GitHub Copilot, Claude Code, and more — enabling natural language data modeling, queries, security config, and docs generation.
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·3 min
Connecting Zotero to AI via MCP: A Complete Zero-Cost Guide to Generating Real Literature Reviews
Connect your local Zotero library to AI via MCP protocol to eliminate hallucinated citations. Full guide covering UV, Zotero setup, and OpenCode with free DeepSeek.
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·3 min
LangChain + MCP + Agent: A Complete Guide to Enterprise-Grade AI Agent Development
A deep dive into LangChain, LangGraph, MCP, and enterprise AI Agent development: covering Streamable HTTP updates, DeepSeek R1 Function Calling limits, and Qwen3 agent capabilities.
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Claude Code Practical Guide: Environment Setup, Git Workflows, and Engineering in Production
A complete guide to Claude Code: environment setup, switching to domestic LLMs, CLI commands, Git workflows, MCP, Subagents, and enterprise project walkthroughs.
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AI-Powered Automated Testing: Using Pytest + MCP to Tackle Non-Determinism
Deep dive into AI-era automated testing: using Pytest + Playwright + MCP for stable automation, constraining code conventions with Skills, avoiding non-determinism and high token costs. Includes real debugging war stories.
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Java AI Tech Stack Explained: Framework Selection & Learning Path Guide
A complete guide to Java AI development: Spring AI, LangChain4j, Spring AI Alibaba, and AgentScope4j — framework comparisons, selection tips, and a clear learning path.
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·4 min
The Complete Guide to Claude Code Skills: Installation, Creation, and Management
A complete guide to Claude Code Skills: what they are, how they differ from Plugins, three installation methods, how to write SKILL.md, trigger mechanisms, and top resource recommendations.
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GitHub Daily · July 20: AI Agent Infra…
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GitHub Daily · July 20: AI Agent Infrastructure Explodes Across the Board
AI Agent infrastructure explodes across GitHub Trending: OmniRoute unifies 268+ providers, cognee adds long-term memory, and self-hosted openship tops growth with +1719 stars.
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·3 min
Frontend to AI Agent: Why TypeScript + Zod Have Become Non-Negotiable
Frontend engineers pivoting to AI Agent development: TypeScript and Zod are now must-have skills. Explore the full progression from API calls to building LangGraph-style frameworks, and nail the 3 core interview topics.
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Fuse: An Open-Source MCP Tool Built to…
·2 min
Fuse: An Open-Source MCP Tool Built to Optimize Claude Code for C# Codebases
Fuse is an open-source MCP/CLI tool that improves Claude Code's performance on large C# codebases using Roslyn-powered semantic queries to reduce token usage.
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PostHog Deep Dive: The Open-Source All…
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PostHog Deep Dive: The Open-Source All-in-One Platform for Product Analytics and AI Observability
PostHog is an open-source all-in-one platform combining product analytics, session replay, feature flags, A/B testing, and AI Observability. Supports self-hosting and MCP protocol.
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Code Review Graph: Using Intelligent C…
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Code Review Graph: Using Intelligent Code Graphs to Deliver Precise Context for AI
Code Review Graph is a local-first open-source code intelligence graph supporting MCP and CLI. It reduces AI context noise in large repos with persistent graph structures.
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Build an MCP Server in 10 Minutes: Let AI Call Your Code Directly
MCP (Model Context Protocol) is the open standard for AI tool integration. Build your own MCP server with ~20 lines of Python. Learn tools, resources, prompts, and both local and remote deployment.
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·3 min
Cherry Studio + MCP: A Practical Guide to Building AI Agents with Local Deployment
Learn how to build an automated AI agent using Cherry Studio, MCP protocol, and locally deployed models — covering DeepSeek integration, web scraping, and private knowledge base setup.
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Cherry Studio + MCP + Ollama: The Complete Guide to Building a Local AI Agent and Private Knowledge Base
A hands-on guide to building a local AI agent and private knowledge base using Cherry Studio, MCP, and Ollama — with web scraping, report generation, and terminal control.
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GitHub Daily · July 17: AI Coding Infr…
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GitHub Daily · July 17: AI Coding Infrastructure Explodes — From Context Graphs to Vector Indexes
AI coding infrastructure explodes on GitHub: context management, code graphs, and vector indexes become the new battleground as the community shifts from apps to underlying capabilities.
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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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