memocean-mcp 0.5.0

Local-first AI memory MCP server for Obsidian vaults. CJK-optimized.

memocean-mcp — Local-first AI Memory MCP Server

A local MCP server for AI agents to search and manage knowledge stored in Obsidian vaults. Built for CJK (Chinese/Japanese/Korean) developers — 94.4% Hit@5 on Chinese queries with zero AI components required.

Core features:

• BM25/INSTR hybrid search — CJK-optimized, pure SQLite, no embeddings needed
• CLSC sonar compression — 92.5% token reduction (13x compression) on Obsidian notes
• Temporal knowledge graph — entity-relationship store with non-destructive invalidation
• Cross-agent memory sharing — multiple agents share one memory.db
• FATQ task queue — File-Atomic Task Queue for agent coordination

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Quick Start

pip install memocean-mcp

Add to Claude Desktop / Claude Code .mcp.json:

{
  "mcpServers": {
    "memocean": {
      "command": "memocean-mcp",
      "env": {
        "MEMOCEAN_VAULT_ROOT": "/path/to/your/obsidian/vault"
      }
    }
  }
}

Or register with Claude Code CLI:

MEMOCEAN_VAULT_ROOT=/path/to/vault claude mcp add memocean memocean-mcp

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Environment Variables

Variable	Default	Description
MEMOCEAN_VAULT_ROOT	~/Documents/Obsidian Vault	Root of your Obsidian vault
MEMOCEAN_DATA_DIR	~/.memocean	Data directory (databases, task queue)
MEMOCEAN_VAULT_PATH	MEMOCEAN_VAULT_ROOT/Ocean	Ocean subdirectory for full-text search
MEMOCEAN_SKILLS_DIR	MEMOCEAN_VAULT_ROOT/Ocean/Pearl/skills	Skills markdown directory
MEMOCEAN_USE_GBRAIN	false	Enable GBrain hybrid search delegate
KNN_ENABLED	false	Enable BGE-m3 KNN vector search
ENABLE_QUERY_EXPANSION	unset	Enable Haiku query expansion (requires ANTHROPIC_API_KEY)
ENABLE_HAIKU_RERANKER	unset	Enable Haiku LLM reranker
ANTHROPIC_API_KEY	unset	Required only for AI-assisted features above

Backward-compat: CHANNELLAB_BOTS_ROOT → MEMOCEAN_DATA_DIR, CHANNELLAB_OCEAN_VAULT_ROOT → MEMOCEAN_VAULT_ROOT.

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Available Tools

Tool	Description
memocean_search	Unified search across Radar (sonar index) + message history. Default entry point.
memocean_radar_search	Search CLSC sonar index — fast keyword search, ~13% of verbatim token cost.
memocean_seabed_get	Retrieve full content by slug (verbatim or sonar mode).
memocean_ocean_search	Full-text search over Ocean vault .md files via ripgrep.
memocean_messages_search	BM25 search over cross-agent message history.
memocean_kg_query	Query the temporal knowledge graph by entity name.
memocean_skill_list	List or retrieve approved skills from the skill library.
memocean_task_create	Create a task in the FATQ pending queue (agent coordination).
memocean_ingest_file	Ingest local file (PDF/DOCX/XLSX/HTML/CSV/JSON) into Radar via MarkItDown.
memocean_report_store	Store a verbatim markdown report into Ocean vault Reports folder.

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Search Architecture

Two-path retrieval, zero AI dependency by default:

CJK query  →  SQLite INSTR on radar.clsc  →  ranked by match_count
EN query   →  FTS5 BM25                   →  fallback to INSTR on miss

Benchmark (pure BM25/INSTR, no AI):

Dataset	Language	Hit@5
Internal corpus	Chinese (mixed)	94.4%
DRCD	Traditional Chinese	91.9%
CMRC	Simplified Chinese	93.3%
BEIR SciFact	English	70.7%

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CLSC Sonar Compression

CLSC (Closet Lossy Summary for Chinese) extracts each document into a compact single-line sonar entry. Format:

[SLUG|ENTITIES|topics|"key_quote"|WEIGHT|EMOTIONS|FLAGS]

Compression ratio: 1,716,211 raw tokens → 129,529 sonar tokens = 13x (92.5% reduction).

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Requirements

• Python 3.11+
• SQLite 3.35+
• Optional: markitdown[all] for file ingestion
• Optional: anthropic package for AI-assisted features (query expansion, reranking)

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License

MIT — see LICENSE.

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Acknowledgements

Built on MemPalace (dual-layer architecture, AAAK skeleton format) and inspired by GBrain (Compiled Truth + Dream Cycle design).