choihyunsus

choihyunsus / n2-QLN

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n2-QLN: Intelligent Tool Router & Semantic Search Layer for MCP. Connect 1,000+ tools through 1 interface. Prevent AI model confusion and maximize context window efficiency

nton2.com ai-agent-orchestration efficiency llm-gateway mcp semantic-search
23
2
100% credibility
Found Mar 22, 2026 at 23 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
JavaScript
AI Summary

n2-qln is a semantic search layer that routes natural language queries to the most relevant tools for AI agents via a single unified interface.

How It Works

1
🔍 Discover QLN

You learn about this smart organizer that lets your AI helper manage hundreds of useful actions without getting overwhelmed.

2
📱 Link to your AI app

You simply update the settings in your AI desktop program like Claude or Cursor to include this helpful organizer.

3
✨ One magic tool unlocks all

Restart your app, and your AI now has a single clever tool that knows about and can use every other tool you add.

4
💭 Tell it what you need

Chat with your AI about a task, like capturing a picture of your screen, and it quickly searches for the right helper.

5
🚀 It finds and does it

The organizer picks the best match in a flash and runs the action, giving you the result right away.

6
➕ Grow your toolkit

Describe new helpers to your AI in plain words, and it adds them to the collection automatically.

🎉 AI superpowers unlocked

Your AI now handles endless tasks smoothly and learns from what you use most, making everything faster and easier.

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Star Growth

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AI-Generated Review

What is n2-QLN?

n2-QLN is a JavaScript layer for MCP that connects 1,000+ tools through one intelligent interface, so your AI model only sees a single `n2_qln_call` tool instead of a bloated list. It prevents model confusion by routing queries via semantic search and keyword matching, slashing context from 50k+ tokens to ~200 for massive efficiency gains. Install via npm, hook it into Claude Desktop or Cursor, and register tools dynamically with actions like search, exec, create, or delete.

Why is it gaining traction?

It stands out by delivering sub-5ms searches across thousands of tools without native dependencies or restarts, using optional Ollama for semantic boosts while gracefully degrading to keyword ranking. Self-learning ranks popular tools higher, and provider JSONs auto-index at boot—perfect for scaling MCP setups without context overload. Developers dig the 99.6% token reduction that keeps AI responses snappy.

Who should use this?

AI agent builders integrating MCP in tools like Claude Desktop, Cursor, or n2-soul, especially those juggling dozens of web, data, or dev tools. Backend devs exposing HTTP endpoints or local functions via one router, or anyone hitting context limits with growing toolsets.

Verdict

Worth a spin for MCP-heavy workflows—solid docs and npm-ready, but 23 stars and 1.0% credibility signal early maturity; test in prod before committing. Promising for maximizing context efficiency if your stack aligns.

(178 words)

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