#qwen

MCP Python Interpreter: run python code.

Local LLM Server with NPU Acceleration

OpenAI Agent SDK + MCP servers on local Qwen3 (with ollama)

MCP 资源精选， MCP指南，Claude MCP，MCP Servers, MCP Clients

A Chat Client for LLMs, written in Compose Multiplatform.

MCP 资源精选， MCP指南，Claude MCP，MCP Servers, MCP Clients

MCP client/server example using qwen3

Stop letting AI hallucinations eat your hours. With this engine, your work flows smoothly while a full SRE squad of models codes and debugs on your behalf. Are you building complex applications, only to find that AI hallucinations are eating your entire afternoon? You know the loop: You ask Claude or Cursor to fix a bug. It gives you a snippet. It breaks something else. You paste the error back. It forgets the original architecture and responds with "// ... rest of your code here". What started as a 5-minute feature turns into a 3-hour circular debugging nightmare. If this engine actually works, you are saved. The Qwen Engineering Engine (powered by the Lachman Protocol) completely stops the "two steps forward, one step back" dance. Instead of relying on a single, forgetful LLM to do everything, this MCP Server deploys a dedicated, specialized squad of Qwen models to your local codebase: - Zero Placeholders: The dedicated qwen_coder tool writes 100% complete, production-grade files. No lazy snipping. - Deep Debugging: Instead of pasting logs to Claude, the qwen_audit tool unleashes QwQ (Qwen's reasoning model) to act as your Senior Auditor. It reads the files, finds the memory leak, and tells you exactly what failed. - Architectural Immunity: Before writing code, the qwen_architect drafts a JSON roadmap and self-verifies it against your stack. If it's a bad idea, it rejects it *before* breaking your app. Why Qwen? Because running an entire squad of GPT-4o or Claude 3.5 Opus models to constantly rewrite files would cost you $50 a day. By routing this heavy lifting through Alibaba's DashScope API (Qwen 3.5 Plus & Qwen 2.5 Coder 32B), the cost is literal fractions of a cent. Let your main assistant (Claude/Antigravity/Cursor) be the Commander. Let the Qwen Engine do the heavy lifting in the trenches. Stop chatting. Start shipping.

Transform your AI agents into Apple development experts! Apple RAG MCP gives you instant access to official Swift docs, design guidelines, and comprehensive Apple platform knowledge through cutting-edge RAG technology. With professional AI reranking and hybrid search across iOS, macOS, watchOS, tvOS, and visionOS documentation plus Apple Developer YouTube content, you'll get precise, contextual answers every time. Compatible with Cursor, Claude Desktop, and all MCP tools - start building smarter Apple apps today!

💬 MaxKB is an open-source AI assistant for enterprise. It seamlessly integrates RAG pipelines, supports robust workflows, and provides MCP tool-use capabilities.

a magical desktop app that puts the power of LLMs and MCP in the hands of everyone