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KVANTRA-dev/NOUZ-MCP

v3.2.2 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 17d MCP Data & Storage
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Summary

AI summary

search_chunks gains configurable score_mode and diagnostic metadata.

Changes in this release

Feature Medium

Added score_mode=auto/raw/centered to search_chunks.

Added score_mode=auto/raw/centered to search_chunks.

Source: granite4.1:8b-q6_K@2026-05-21

Confidence: high

Feature Medium

Scoped path searches keep raw cosine by default; centered scoring can be forced manually.

Scoped path searches keep raw cosine by default; centered scoring can be forced manually.

Source: granite4.1:8b-q6_K@2026-05-21

Confidence: high

Feature Medium

search_chunks now returns diagnostic score metadata: score, score_raw, score_centered, score_mode, candidate_count, centroid_norm, and score_gap.

search_chunks now returns diagnostic score metadata: score, score_raw, score_centered, score_mode, candidate_count, centroid_norm, and score_gap.

Source: granite4.1:8b-q6_K@2026-05-21

Confidence: high

Feature Medium

Added scripts/benchmark_chunk_scoring.py for anonymized raw-vs-centered benchmark reports.

Added scripts/benchmark_chunk_scoring.py for anonymized raw-vs-centered benchmark reports.

Source: granite4.1:8b-q6_K@2026-05-21

Confidence: low

Refactor Medium

Unscoped large chunk searches now default to mean-centered cosine scoring.

Unscoped large chunk searches now default to mean-centered cosine scoring.

Source: granite4.1:8b-q6_K@2026-05-21

Confidence: high

Full changelog

Changed

  • Added score_mode=auto/raw/centered to search_chunks.
  • Unscoped large chunk searches now default to mean-centered cosine scoring to reduce anisotropic embedding background.
  • Scoped path searches keep raw cosine by default; centered scoring can still be forced manually.
  • search_chunks now returns diagnostic score metadata: score, score_raw, score_centered, score_mode, candidate_count, centroid_norm, and score_gap.

Added

  • Added scripts/benchmark_chunk_scoring.py for anonymized raw-vs-centered benchmark reports without note paths, headings, titles, or note text.

Verification

  • python -m compileall -q nouz_mcp pytest_smoke.py scripts
  • python -m pytest -q
  • python test_server.py
  • live search_chunks smoke on the local SQLite index
  • python -m build --sdist --wheel
  • python -m twine check

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About KVANTRA-dev/NOUZ-MCP

Semantic knowledge graph for Obsidian. Three modes (pure graph / semantic classification / strict hierarchy)

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