Which integrations are available for this server?

Provides tools for searching and managing knowledge stored in Obsidian vaults, including full-text search, BM25/INSTR hybrid search, and file ingestion into Obsidian.

How do I use memocean-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@memocean-mcp search my notes about machine learning" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

memocean-mcp

by ChannelLabAI

Overview Schema Related Servers Score Discussions

Python

Local

memocean-mcp — Local-first AI Memory MCP Server

License: MIT Python 3.11+

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

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

Related MCP server: obsidian-hybrid-search

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.

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.

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%

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).

Requirements

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

License

MIT — see LICENSE.

Acknowledgements

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

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

1Releases (12mo)

Commit activity

Resources

GitHub Repository

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