nouz-mcp 2.5.1

MCP server for Obsidian — semantic knowledge graph with auto-classification, DAG hierarchy, and cross-domain bridge detection

NOUZ — Semantic MCP Server for Your Knowledge Base

Works with Obsidian, Logseq, and any directory of Markdown files.

Structure emerges from content.

🇷🇺 Русская версия

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Why NOUZ

When your knowledge base grows — organizing documents into folders stops working. Your AI agent sees files, but doesn't understand how your ideas and documents connect.

NOUZ gives your agent semantic coordinates. Each note gets a domain sign, a hierarchy level, and connections to other notes. The domain is assigned from the file's content — or manually by you, if you prefer strict hierarchy.

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What It Does

NOUZ sits between your note base and your AI agent. It handles all the chaos-structuring:

1. Automatic Classification (Semantics)
   You define "Cores" — base domains of your interests (e.g., 🧠 Systems Thinking, 🧬 Science, 💻 Code). When you add a new note, NOUZ reads its text, compares vectors, and automatically assigns the correct domain (Sign) or a combination of domains.

2. Hidden Connection Discovery (Bridges)
   The server doesn't just build a directed graph (DAG). It finds non-obvious intersections between disciplines:

   • Semantic bridges: two notes from different domains talk about the same thing.
   • Tag bridges: notes share hidden concepts at the tag level.
   • Analogies: notes play the same structural role in different sciences (e.g., "framework" in IT and "taxonomy" in biology).

3. Base Evolution Tracking (Drift)
   NOUZ aggregates data bottom-up. If your "Philosophy" folder started accumulating too many notes about algorithms, the system notices and shows the divergence (core drift) — your folder evolved.

Depending on your needs, NOUZ works in three modes: from a simple visual graph (LUCA) to a strict 5-level self-organizing hierarchy (SLOI).

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How It Works

1. You describe domains in config.yaml — what each does, what language it speaks.
2. The server turns descriptions into vector etalons (locally, via LM Studio or Ollama).
3. Each new note is projected onto these axes. Sign is determined by content, or by you.
4. Modules automatically receive core_mix — aggregated core composition from all their quants. If a module's sign diverges from core_mix — the server reports core_drift.

Three types of bridges find connections between notes from different domains: semantic (texts are close), tag (concepts overlap), analogy (similar role in the graph).

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Quick Start

pip install nouz-mcp
OBSIDIAN_ROOT=/path/to/vault nouz-mcp

Without config.yaml, the server starts in LUCA mode — graph without semantics, works immediately.

Or from source:

git clone https://github.com/KVANTRA-dev/NOUZ-MCP
cd NOUZ-MCP
pip install -r requirements.txt
OBSIDIAN_ROOT=./vault python server.py

Connect to Claude Desktop, Cursor, OpenCode, or any MCP client:

{
  "mcpServers": {
    "nouz": {
      "command": "nouz-mcp",
      "env": {
        "OBSIDIAN_ROOT": "/path/to/vault",
        "MODE": "prizma",
        "EMBED_API_URL": "http://127.0.0.1:1234/v1"
      }
    }
  }
}

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MCP Tools

Tool	Purpose
suggest_metadata	Sign, level, bridges, drift warnings
write_file	Write a note with YAML frontmatter
read_file	Read a note + metadata
calibrate_cores	Update core reference vectors
recalc_signs	Recalculate signs for all notes
recalc_core_mix	Recalculate bottom-up aggregation
index_all	Re-index the entire base
format_entity_compact	Formula (children)[sign]{parents}
embed	Get a vector for text
list_files	List with filters by level, sign
get_children / get_parents	Graph traversal
suggest_parents	Find parents for an orphan
add_entity	Create an entity in one step (auto sign, tags, parents)
process_orphans	Auto-fill files without markup

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Configuration

Minimal config.yaml:

mode: prizma

etalons:
  - sign: S
    name: Systems Thinking
    text: >
      Methodology for analysing complex objects: feedback loops,
      emergent properties, self-regulation, bifurcation points.
      Cybernetics, synergetics, dissipative structures — tools for
      understanding how the whole exceeds the sum of its parts.
      Not data and not code — a way of thinking about complexity.
  - sign: D
    name: Data & Science
    text: >
      Physics and cosmology: Lagrangians, curvature tensors, quarks,
      fermions, plasma, vacuum fluctuations, cosmic microwave background.
      Pure science about the nature of matter, energy and spacetime.
  - sign: E
    name: Engineering
    text: >
      Software engineering, ML, infrastructure: writing and debugging
      code, deployment, containerisation, neural networks, inference,
      microservices, CI/CD, refactoring, APIs. The practical discipline
      of building computational systems from architecture to production.

thresholds:
  sign_spread: 0.05
  confident_spread: 60.0
  pattern_second_sign_threshold: 30.0
  semantic_bridge_threshold: 0.55
  structural_bridge_threshold: 0.55
  parent_link_threshold: 0.55

After setup, run calibrate_cores — the server creates reference vectors. Check pairwise cosines: mean-centered between different domains should be noticeably lower than raw. If all pairs are roughly equal — strengthen the differences in texts.

Real Calculation Example

Here are actual results for the S/D/E etalons using the text-embedding-granite-embedding-278m-multilingual model:

=== Pairwise Cosine (raw) ===
S↔D: 0.5890    S↔E: 0.5853    D↔E: 0.6011

=== Pairwise Cosine (mean-centered) ===
S↔D: -0.5051   S↔E: -0.5120   D↔E: -0.4827

Negative mean-centered values are an excellent result: cores are semantically well-separated. Self-classification: S→99.2%, D→97.6%, E→96.9%.

Variable	Default	Description
OBSIDIAN_ROOT	./obsidian	Path to vault
MODE	luca	luca, prizma, or sloi
EMBED_PROVIDER	openai	openai, lmstudio, ollama
EMBED_API_URL	http://127.0.0.1:1234/v1	Embedding endpoint
EMBED_API_KEY	(empty)	API key, if needed
EMBED_MODEL	(empty)	Model name

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Privacy

Component	Local?
Embeddings (LM Studio / Ollama)	✅ Yes
Your notes	✅ Yes
NOUZ server	✅ Yes
AI agent context (Claude, ChatGPT)	❌ Goes to cloud

Everything critical stays on your machine.

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Development

git clone https://github.com/KVANTRA-dev/NOUZ-MCP
cd NOUZ-MCP
pip install -e .
python -m pytest test_server.py

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Links

• 🌐 kvantra.tech
• 📦 PyPI
• 🗂️ Glama Registry
• 💬 Telegram
• 🐙 GitHub
• 📄 Paper "Recursive Self-Organization as a Universal Principle"

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MIT License © 2026 KVANTRA

Cosines are computed. Syntax changes. Semantics remains.