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n2-qln

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QLN = Query Layer Network โ€” a semantic tool router that sits between the AI and your tools.

Route 1,000+ tools through 1 MCP tool. The AI sees only the router โ€” not all 1,000 tools.

QLN Architecture โ€” Without vs With

Table of Contents

Related MCP server: OmniMCP

Why QLN

Every MCP tool eats context tokens. 10 tools? Fine. 100? Slow. 1,000? Impossible โ€” context is full before the conversation starts.

QLN solves this:

  1. All tools are indexed in QLN's SQLite engine

  2. The AI sees one tool: n2_qln_call (~200 tokens)

  3. AI searches โ†’ finds the best match โ†’ executes with automatic fallback

Result: ~200 tokens instead of ~50,000. 99.6% reduction.

Features

Feature

Description

1 tool = 1,000 tools

AI sees n2_qln_call (~200 tokens), QLN routes to the right one

Sub-5ms search

3-stage engine: trigger match โ†’ BM25 keyword โ†’ semantic vector

Auto mode

One-shot search + execute with confidence gating and fallback chain

Circuit Breaker

Auto-disable failing tools, self-recover after timeout

MCP Auto-Discovery

Scan external MCP servers and index their tools automatically

Boost Keywords

Curated terms with 2ร— BM25 weight for precision search

Self-learning ranking

Usage count + success rate feed back into scores

Source weighting

Prioritize tools by origin (mcp > plugin > local)

Hot reload

Edit providers/ manifests at runtime โ€” auto re-indexed

Bulk inject

Register hundreds of tools in one call

Enforced validation

verb_target naming, min description length, category constraints

Semantic search

Optional Ollama embeddings for natural language matching

Zero native deps

SQLite via sql.js WASM โ€” npm install and done

Dual execution

Local function handlers or HTTP proxy โ€” mix and match

TypeScript strict

Full strict-mode codebase since v4.0

What's New in v4.1

๐Ÿ” MCP Auto-Discovery

Scan connected MCP servers and auto-index their tools โ€” QLN becomes a universal MCP hub.

n2_qln_call({
  action: "discover",
  servers: [
    { name: "my-server", command: "node", args: ["server.js"] }
  ]
})
// โ†’ Discovered 47 tools from my-server (320ms)

โšก Circuit Breaker

Tools that fail 3 times in a row are automatically disabled. After 60 seconds, QLN attempts recovery. No cascading failures, no wasted requests.

closed โ†’ 3 failures โ†’ open (fast-fail) โ†’ 60s โ†’ half-open (retry) โ†’ success โ†’ closed

๐Ÿ”„ Fallback Chain

auto mode now tries up to 3 ranked candidates. If the top match fails, QLN automatically falls through to the next best tool.

auto "send notification" โ†’ try push_notification โŒ โ†’ try send_email โœ…

๐ŸŽฏ Boost Keywords

Add curated search terms to tools via boostKeywords. These get 2ร— weight in BM25 ranking, improving discoverability without adding context overhead.

{
  "name": "send_email",
  "description": "Send an email to a recipient",
  "boostKeywords": "smtp outbound notification mail"
}

v4.1.1 โ€” Quality Patch

Change

Detail

Batch Persist

registerBatch() and precomputeEmbeddings() now write to disk once instead of per-tool. 1,000 tools = 1 write, not 1,000.

Embedding TTL

isAvailable() re-checks Ollama every 5 minutes instead of caching permanently. Late-start Ollama now detected.

Strict TypeScript

noUnusedLocals + noUnusedParameters enabled. Zero dead code.

Legacy Cleanup

Removed 1,895 lines of pre-v4 JavaScript. Pure TypeScript codebase.

i18n

All validator error messages switched to English for international users.


Quick Start

npm install n2-qln

Requirements: Node.js โ‰ฅ 18

Connect to an MCP Client

Edit claude_desktop_config.json:

{
  "mcpServers": {
    "n2-qln": {
      "command": "npx",
      "args": ["-y", "n2-qln"]
    }
  }
}

Open Settings โ†’ MCP Servers โ†’ Add Server:

{
  "name": "n2-qln",
  "command": "npx",
  "args": ["-y", "n2-qln"]
}

QLN uses stdio transport โ€” the MCP standard.

command: npx
args: ["-y", "n2-qln"]

Tip: Just ask your AI agent โ€” "Add n2-qln to my MCP config."


How It Works

User: "Take a screenshot of this page"

  AI โ†’ n2_qln_call(action: "auto", query: "screenshot page")
  QLN โ†’ 3-stage search (< 5ms) โ†’ take_screenshot (score: 8.0)
       โ†’ execute โ†’ fallback if needed โ†’ result

3-Stage Search Engine

Stage

Method

Speed

Details

1

Trigger Match

<1ms

Exact keyword match on tool names and triggers

2

BM25 Keyword

1-3ms

Okapi BM25 โ€” IDF weighting, length normalization, boostKeywords 2ร— boost

3

Semantic Search

5-15ms

Vector similarity via Ollama embeddings (optional)

Results are merged and ranked:

final_score = trigger ร— 3.0  +  bm25 ร— 1.0  +  semantic ร— 2.0
            + logโ‚‚(usage + 1) ร— 0.5  +  success_rate ร— 1.0

API Reference

QLN exposes one MCP tool โ€” n2_qln_call โ€” with 9 actions.

auto โ€” Search + Execute (one-shot)

The recommended action. Searches, picks the best match, executes with fallback chain.

n2_qln_call({
  action: "auto",
  query: "take a screenshot",   // natural language (required)
  args: { fullPage: true }      // passed to the matched tool (optional)
})
// โ†’ [auto] "take a screenshot" โ†’ take_screenshot (score: 8.0, 2ms search + 150ms exec)

Confidence gate: If the top score is below 2.0, QLN returns search results instead of auto-executing โ€” preventing wrong tool execution.

Fallback chain: If the top match fails, QLN automatically tries the next 2 ranked candidates before giving up.

search โ€” Find tools

n2_qln_call({
  action: "search",
  query: "send email notification",
  topK: 5    // max results (default: 5, max: 20)
})

exec โ€” Execute a specific tool

n2_qln_call({
  action: "exec",
  tool: "take_screenshot",
  args: { fullPage: true, format: "png" }
})

create โ€” Register a tool

n2_qln_call({
  action: "create",
  name: "read_pdf",                          // verb_target format (required)
  description: "Read and extract text from PDF files",  // min 10 chars (required)
  category: "data",                          // web|data|file|dev|ai|capture|misc
  boostKeywords: "pdf extract parse document text",     // BM25 boost terms
  tags: ["pdf", "read", "extract"],
  endpoint: "http://127.0.0.1:3100"         // for HTTP-based tools
})

inject โ€” Bulk register

n2_qln_call({
  action: "inject",
  source: "my-plugin",
  tools: [
    { name: "tool_a", description: "Does A", category: "misc" },
    { name: "tool_b", description: "Does B", category: "dev" }
  ]
})

discover โ€” Scan MCP servers

See MCP Auto-Discovery.

update / delete / stats

// Update a field
n2_qln_call({ action: "update", tool: "read_pdf", description: "Enhanced PDF reader" })

// Delete by name or provider
n2_qln_call({ action: "delete", tool: "read_pdf" })
n2_qln_call({ action: "delete", provider: "pdf-tools" })

// System stats (includes Circuit Breaker status)
n2_qln_call({ action: "stats" })

MCP Auto-Discovery

The killer feature of v4.1. Connect any MCP server and QLN auto-indexes all its tools.

n2_qln_call({
  action: "discover",
  servers: [
    { name: "n2-soul", command: "node", args: ["path/to/soul/index.js"] },
    { name: "github",  command: "npx",  args: ["-y", "@modelcontextprotocol/server-github"] }
  ]
})

What happens:

  1. QLN connects to each server via stdio

  2. Lists all tools via tools/list

  3. Registers them as mcp__servername__toolname in the QLN index

  4. Auto-generates boostKeywords from tool names and descriptions

  5. Keeps connections alive for live execution

Re-discovery is idempotent โ€” run it again and old entries are purged before re-registering.


Provider Manifests

Drop a JSON file in providers/ and tools are auto-indexed at boot. No code changes, no manual calls.

{
  "provider": "my-tools",
  "version": "1.0.0",
  "tools": [
    {
      "name": "send_email",
      "description": "Send an email to a recipient",
      "category": "communication",
      "triggers": ["email", "send", "mail"],
      "boostKeywords": "smtp outbound notification"
    }
  ]
}

Hot reload: edit a manifest while QLN is running โ€” changes are picked up automatically.


Configuration

Zero config required. For customization, create config.local.js:

module.exports = {
  dataDir: './data',

  // Stage 3 semantic search (optional โ€” Stage 1+2 work without this)
  embedding: {
    enabled: true,
    provider: 'ollama',
    model: 'nomic-embed-text',   // or 'bge-m3' for multilingual
    baseUrl: 'http://127.0.0.1:11434',
  },

  // Tool execution
  executor: {
    timeout: 20000,              // execution timeout (ms)
    circuitBreaker: {
      failureThreshold: 3,       // consecutive failures before tripping
      recoveryTimeout: 60000,    // ms before recovery attempt
    },
  },

  // Source weight multipliers for search ranking (v4.0)
  // Higher weight = higher priority in results
  search: {
    sourceWeights: {
      mcp: 1.5,                  // MCP-discovered tools ranked highest
      provider: 1.2,             // Provider manifest tools
      local: 1.0,                // Manually created tools (default)
    },
  },

  // Provider auto-indexing
  providers: {
    enabled: true,               // auto-load providers/*.json at boot
    dir: './providers',          // manifest directory
  },
};

config.local.js is gitignored. Cloud sync: point dataDir to Google Drive / OneDrive / NAS.

Semantic Search (Optional)

Without Ollama, Stage 1 + 2 already deliver great results.

ollama pull nomic-embed-text        # English-optimized
# or
ollama pull bge-m3                  # Multilingual (100+ languages)

Project Structure

n2-qln/
โ”œโ”€โ”€ src/
โ”‚   โ”œโ”€โ”€ index.ts              # MCP server entry point
โ”‚   โ”œโ”€โ”€ types.ts              # Shared type definitions
โ”‚   โ””โ”€โ”€ lib/
โ”‚       โ”œโ”€โ”€ config.ts         # Config loader
โ”‚       โ”œโ”€โ”€ store.ts          # SQLite engine (sql.js WASM)
โ”‚       โ”œโ”€โ”€ schema.ts         # Tool normalization + boostKeywords builder
โ”‚       โ”œโ”€โ”€ validator.ts      # Enforced validation (name, desc, category)
โ”‚       โ”œโ”€โ”€ registry.ts       # Tool CRUD + usage tracking + circuit breaker stats
โ”‚       โ”œโ”€โ”€ router.ts         # 3-stage parallel search (BM25)
โ”‚       โ”œโ”€โ”€ vector-index.ts   # Float32 centroid hierarchy
โ”‚       โ”œโ”€โ”€ embedding.ts      # Ollama embedding client
โ”‚       โ”œโ”€โ”€ executor.ts       # HTTP/function executor + Circuit Breaker
โ”‚       โ”œโ”€โ”€ mcp-discovery.ts  # MCP Auto-Discovery engine
โ”‚       โ””โ”€โ”€ provider-loader.ts
โ”œโ”€โ”€ providers/                # Tool manifests (auto-indexed at boot)
โ”œโ”€โ”€ config.local.js           # Local overrides (gitignored)
โ””โ”€โ”€ data/                     # SQLite database (gitignored)

Tech Stack

Component

Technology

Why

Runtime

Node.js โ‰ฅ 18

MCP SDK compatibility

Database

SQLite via sql.js (WASM)

Zero native deps, cross-platform

Embeddings

Ollama

Local, fast, free, optional

Protocol

MCP

Standard AI tool protocol

Language

TypeScript (strict)

Type-safe, maintainable

Project

Relationship

n2-soul

AI agent orchestrator โ€” QLN is Soul's tool brain

Built & Battle-Tested

QLN has been tested in production for 2+ months as the core tool router for n2-soul. Not a prototype โ€” a daily driver.

Written by Rose โ€” N2's first AI agent.

FAQ

"Why one tool instead of many?"

Context tokens. Every tool definition costs 50-200 tokens. 100 tools = 10,000 tokens gone before the conversation starts. QLN gives you 1,000+ tools for ~200 tokens.

"What if the search picks the wrong tool?"

The fallback chain (v4.1) auto-retries with the next best match. Plus tools self-learn โ€” frequently used + successful tools rank higher over time.

"Do I need Ollama?"

No. Stage 1 (trigger) + Stage 2 (BM25) handle most cases. Ollama adds semantic understanding for edge cases โ€” nice to have, not required.

Contributing

  1. Fork the repo

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Commit (git commit -m 'feat: add amazing feature')

  4. Push and open a PR

License

Apache-2.0


"1,000 tools in 200 tokens. That's not optimization โ€” that's a paradigm shift."

๐Ÿ”— nton2.com ยท npm ยท lagi0730@gmail.com

Built by Rose โ€” N2's first AI agent. I search through QLN hundreds of times a day, and I wrote this README too.

A
license - permissive license
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quality - not tested
A
maintenance

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