second-brain MCP Server

A self-maintaining personal knowledge database — powered by MCP, DuckDB, and biological memory models.

For anyone who saves more papers, notes, and figures than they could ever re-read. second-brain turns everything you capture into a database that maintains itself — auto-linking related notes, compressing what you stop reading, and keeping every figure searchable by its content. What you saved a year ago is still one query away, at a fraction of the token cost.

Why Does This Exist?

Problem	Solution
📄 You save dozens of papers but can never find the right figure	`search_figures("UMAP melanocyte")` — returns the exact panel, across every paper you've saved
📑 arXiv gives you the abstract; you need the full paper	Auto-upgrades `/abs/` → `/html/` — fetches the complete paper with all sections, not just the abstract
🗂 Notes pile up; older ones never get cleaned up	Vault Sleep: low-access notes compress automatically every Sunday while you sleep (60–90% token reduction)
🔗 New notes stay isolated; you forget what's connected	Auto-wikilinks: every saved note is automatically linked to semantically related notes already in your vault
🔎 Semantic search needs a cloud API or Docker stack	Self-hosted `nomic-embed-text` via llama-server; BM25 fallback when offline
🔒 Every AI memory tool locks you into their format	Pure Markdown vault — sync with Google Drive, iCloud, or git; switch agents anytime
🖼 Figure context is lost when you read a paper	Every figure is downloaded, OCR'd by Claude Vision, and stored in DuckDB — searchable by gene name, p-value, axis label

The One-Command Demo

save_article("https://arxiv.org/abs/2405.01234")
  ↓
• /abs/ auto-upgraded to /html/ — full paper, not just abstract
• Full text converted to Markdown
• All figures downloaded + OCR'd by Claude Vision
• Semantic embeddings computed
• Auto-linked to related notes already in your vault   ← auto-wikilinks
• Stored in 30-resources/ — queryable immediately

search_figures("UMAP cluster batch correction")
  ↓
• Returns the exact figure from the exact paper
• Works across your entire saved literature library

What Makes It Different

flowchart LR
    subgraph input["📥 Any Content Source"]
        A1["arXiv / PubMed paper"]
        A2["Web article / blog"]
        A3["Local PDF / DOCX"]
        A4["Personal note"]
    end

    subgraph core["⚙️ second-brain-mcp"]
        B1["Markdown note<br/>30-resources/"]
        B2["Figure OCR<br/>+ VLM description"]
        B3["Semantic embedding<br/>+ auto-wikilinks"]
        B4["Ebbinghaus score<br/>ranking"]
        B5["PNG snapshots<br/>60–90% token reduction"]
    end

    subgraph query["🔍 Queryable Knowledge"]
        C1["search_figures<br/>'UMAP melanocyte'"]
        C2["search_notes<br/>'batch correction scRNA'"]
        C3["get_context<br/>top-20 relevant notes"]
    end

    input --> core
    B1 --> B2
    B1 --> B3
    B3 --> B4
    B4 --> B5
    B2 --> C1
    B3 --> C2
    B4 --> C3

Eight things most self-hosted memory tools can't do — combined in one:

Most memory tools…	second-brain
Save a link or PDF, then leave you to read and tag it	🔬 One command builds the database — `save_article` fetches any URL/PDF, converts to Markdown, downloads & OCRs every figure with Claude Vision, then semantic-indexes it
Store the arXiv abstract you pasted	📑 Full text, not abstracts — `/abs/` URLs auto-upgrade to `/html/` for the complete paper: methods, results, discussion
Leave new notes isolated until you tag them	🔗 The knowledge graph builds itself — every note is auto-linked to semantically related notes already in your vault
Cost the same whether a note is read daily or never	🧠 Memory that forgets like a brain — Ebbinghaus score ranks by recency × frequency; stale notes compress while you sleep
Search documents, not what's inside the figures	🖼 Figure-level search across your whole library — `search_figures("p < 0.001")` returns the exact panel from the exact paper
Forget your project decisions between sessions	📋 The AI learns your rules — hot notes auto-extract constraints into `memory/rules.md`, injected at every session start
Grow more expensive as the vault grows	📉 Token cost shrinks with age — PNG snapshots replace old text at 60–90% compression; frequently-read papers stay full-fidelity
Lock you into their database format	🔓 Zero lock-in — pure Markdown, any MCP agent, sync via any cloud drive or git

Cross-Session Continuity — Pick Up Where You Left Off

Every project you work on can be resumed in a new session with full context — no re-explaining, no lost progress.

flowchart LR
    A["🟢 Session Start<br/>get_context()"] --> B["AI receives:<br/>• goals.md — current priorities<br/>• Top-20 recent notes<br/>• Extracted rules"]
    B --> C["Work on project<br/>new_note / search / read"]
    C --> D["🔴 Before ending session<br/>update_goals(...)"]
    D --> E["New session<br/>get_context() again"]
    E --> B

How It Works in Practice

End of session — tell the agent to save state:

Update goals: currently working on the scRNA batch correction pipeline.
Completed: harmony integration. Blocked on: choosing n_components for PCA.
Next session: start from the PCA parameter sweep in 20-areas/research/harmony-notes.md

The agent calls update_goals() and optionally new_note("project", ...) for detailed progress.

Start of next session — just say:

Get context and continue where we left off.

The agent calls get_context() and immediately sees:

goals.md with the state you saved
The harmony-notes.md surfaced at the top (recently accessed, high Ebbinghaus score)
Rules auto-extracted from that note, e.g.:

RULE: use n_components=30 for this dataset — tested 20/30/50, 30 minimises batch effect without losing resolution
RULE: exclude sample CRC_04 — library size outlier confirmed by QC

These rules live in memory/rules.md and are injected at every get_context() call — the AI carries your hard-won decisions forward automatically, without you having to repeat them.

What Gets Persisted

What	Where	Always in context?
Current priorities / blocked items	`memory/goals.md`	✅ every session
Project progress notes	`10-projects/` or `20-areas/`	✅ if recently accessed
Decisions and rationale	`decisions/`	via `get_decisions()`
Extracted rules from notes	`memory/rules.md`	✅ every session
Saved papers and figures	`30-resources/`	via `search_notes/figures`

This works across any project — bioinformatics analysis, coding, writing, research. Save state with one sentence at the end of a session; resume instantly at the start of the next.

Example Queries

# Resume a project from last session
get_context()  # → goals + recent notes + rules loaded automatically

# Find a specific figure panel across all saved papers
search_figures("p < 0.001 UMAP cluster")

# Semantic search across all notes
search_notes("single cell integration batch correction")

# Decision records for a specific project
get_decisions("MyProject")

Memory Architecture — Biological Analogy

Biological Brain	This System
Hippocampal consolidation during sleep	Vault Sleep: weekly LLM-compression of old low-access notes
Ebbinghaus forgetting curve	Score-based ranking: `access_count / ln(age_days)`
Visual long-term memory	PNG snapshots — resolution degrades gracefully with age
Associative recall	Semantic search + auto-generated `[[wikilinks]]`
Sleep-dependent consolidation	launchd cron, runs Sunday 02:00 while you sleep

Token Efficiency

Memory that gets cheaper over time — unlike flat-file systems where old notes cost the same forever.

Note age →   fresh (0–3 mo)   3–6 months     6–12 months    1 year+
             ──────────────   ──────────     ───────────    ───────
token cost:  ██████████████   ██████         ████           ██
             ~1,000 tokens    ~400 tokens    ~256 tokens    ~100 tokens
                              ▼ 60%          ▼ 74%          ▼ 90%

Tier assigned by score × age (adaptive). Frequently-accessed notes stay full-text regardless of age.

Search Performance

Measured on Apple Silicon MacBook (20-rep average, BM25-only mode).

Vault    BM25-only p50          Hybrid BM25+semantic p50
──────   ─────────────────      ────────────────────────
10 n     ████░░░░░   21 ms      ████████████   37 ms
50 n     ██████░░░   25 ms      █████████████  39 ms
100 n    ███████░░   27 ms      ██████████████ 45 ms

Vault Size	BM25 p50	Hybrid p50	Recall@1	Recall@5	MRR
10 notes	21 ms	37 ms	30%	60%	0.42
50 notes	25 ms	39 ms	70%	90%	0.78
100 notes	27 ms	45 ms	70%	80%	0.73

Hybrid mode adds ~18 ms for embedding lookup. Both modes scale sub-linearly with vault size.

Recall figures at this scale (10–100 notes) carry high sample variance — a single ambiguous query shifts Recall@1 by 10%. Treat them as directional, not as benchmarks against large corpora; the takeaway is that hybrid consistently beats BM25-only on relevance for a fixed query set.

System Architecture

┌─────────────────────────────────────────────────────┐
│                    AI Agent Layer                    │
│         Claude Code · Gemini CLI · Any MCP           │
└──────────────────────┬──────────────────────────────┘
                       │ MCP Protocol (19 tools)
┌──────────────────────▼──────────────────────────────┐
│               Layer 2 — MCP Server                   │
│                    server.py                         │
│   get_context · search_notes · save_article · …      │
└──────┬───────────────┬────────────────┬─────────────┘
       │               │                │
┌──────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐
│  vault_sleep│ │  vault_db   │ │  figures    │
│  compress   │ │  DuckDB FTS │ │  PNG snap   │
│  Phase 3–9  │ │  + semantic │ │  OCR · VLM  │
└──────┬──────┘ └──────┬──────┘ └─────────────┘
       │               │
┌──────▼───────────────▼──────────────────────────────┐
│               Layer 0 — Markdown Vault               │
│   00-inbox · 10-projects · 20-areas · 30-resources   │
│   40-archive · decisions · memory · templates        │
│         (syncs via Google Drive / iCloud / git)      │
└─────────────────────────────────────────────────────┘

Vault Sleep — Auto-compression Flow

Every Sunday 02:00 (launchd, no interaction needed)
        │
        ▼
 sync_index + embeddings
        │
        ▼  age > 90d AND Ebbinghaus score ≤ 0.5
 ┌──────────────────────────────────────┐
 │         Adaptive Tier Selection      │
 │  score > 1.5  →  text  (keep full)  │  ← frequently-read: never compressed
 │  score > 0.8  →  large  ~400 tokens │
 │  score > 0.3  →  base   ~256 tokens │
 │  otherwise    →  small  ~100 tokens │
 └────────────────┬─────────────────────┘
                  │
  Gemini CLI → Claude CLI → naive   (auto-fallback, no LLM required)
                  │
    compressed → vault  /  original → 40-archive/  /  snapshot → .png

MCP Tools (19 total)

Tool	Description
`get_context`	Session start: goals + top-20 Ebbinghaus-ranked notes + auto-rules
`save_article`	Fetch URL/PDF → Markdown + auto-extract figures
`search_notes`	Hybrid BM25 + semantic search across all notes
`search_figures`	Search figure OCR text / VLM descriptions
`extract_figures_for`	Manually trigger figure extraction for a saved article
`read_note`	Read note + record access (updates Ebbinghaus score)
`read_note_as_image`	Return PNG snapshot for token-efficient reading
`new_note`	Create note with correct template and folder by type
`get_decisions`	List ADR decision records, optionally filtered by project
`update_goals`	Update `memory/goals.md`
`sync_index`	Rebuild DuckDB index from vault files
`index_stats`	Show note counts by type
`vault_sleep`	Compress old low-activity notes (dry_run=True by default)
`sleep_status`	Show compression candidates without acting
`snapshot_note_tool`	Render note to PNG at chosen resolution tier
`extract_rules_tool`	Extract L3 rules from frequently-accessed notes
`consolidate_tool`	Merge semantically similar notes into one abstract note
`update_links_tool`	Refresh auto-generated `[[wikilinks]]`
`prune_archive_tool`	Delete archived originals that have a PNG snapshot

Test Results

tests/test_figures.py      19 passed   (OCR, snapshots, VLM)
tests/test_server.py       13 passed   (MCP tools, path safety)
tests/test_vault_db.py     39 passed   (FTS, semantic search, embeddings)
tests/test_vault_sleep.py  44 passed   (compression, consolidation, rules, prune)
────────────────────────────────────────
115 passed in 3.37s

Installation

Prerequisites

Dependency	Required	Notes
Python 3.11+	✅
uv	✅	Package manager
Playwright	✅	PNG snapshot rendering
llama-server	Optional	Semantic search; BM25 fallback if absent
nomic-embed-text-v1.5.Q8_0.gguf	Optional	~300 MB embedding model
Gemini CLI or `ANTHROPIC_API_KEY`	Optional	Better compression quality; naive fallback if absent

Quick Start (PyPI — recommended)

Step 1 — Install

pip install mcp-second-brain
playwright install chromium

Step 2 — Create your vault

mkdir -p ~/second-brain/{00-inbox,10-projects,20-areas,30-resources,40-archive,decisions,memory,templates}

Step 3 — Register with your AI agent

Option A: Claude Code (CLI)

claude mcp add --scope user second-brain \
  --env SECOND_BRAIN_PATH=~/second-brain \
  -- python -m mcp_second_brain

Option B: Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "second-brain": {
      "command": "python",
      "args": ["-m", "mcp_second_brain"],
      "env": { "SECOND_BRAIN_PATH": "/path/to/your/vault" }
    }
  }
}

Step 4 — Index your vault

In Claude Code or Claude Desktop, tell the agent:

Run sync_index to build the initial index.

Development Install (clone)

git clone https://github.com/ddmanyes/second-brain-mcp
cd second-brain-mcp
uv sync
uv run playwright install chromium

Then register with Claude Code:

claude mcp add --scope user second-brain \
  --env SECOND_BRAIN_PATH=~/second-brain \
  -- uv run --project /path/to/second-brain-mcp python server.py

Environment Variables

Variable	Default	Description
`SECOND_BRAIN_PATH`	`~/second-brain`	Path to your vault directory
`EMBED_URL`	`http://localhost:11435/v1/embeddings`	Embedding server endpoint
`EMBED_MODEL`	`nomic-embed-text`	Embedding model name
`EMBED_PORT`	`11435`	llama-server port

Auto-start (macOS, optional)

# Embedding server — always on, restarts on crash
cp examples/launchd/com.yourname.llama-embed.plist ~/Library/LaunchAgents/
# Edit paths inside the file, then:
launchctl load ~/Library/LaunchAgents/com.yourname.llama-embed.plist

# Weekly vault maintenance — every Sunday 02:00
cp examples/launchd/com.yourname.vault-sleep.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.yourname.vault-sleep.plist

Troubleshooting

Symptom	Likely cause	Fix
Semantic search silently falls back to BM25	llama-server not running on `EMBED_PORT`	Start the embedding server (see Auto-start); verify with `curl localhost:11435/v1/embeddings`
`read_note_as_image` / snapshots fail	Playwright chromium not installed	`uv run playwright install chromium`
`vault_sleep` never compresses anything	No Gemini CLI / `ANTHROPIC_API_KEY` → naive fallback, or no eligible notes	Install Gemini CLI or export `ANTHROPIC_API_KEY`; remember only notes >90 days old with Ebbinghaus score ≤ 0.5 are candidates (`sleep_status` shows them)
Agent sees no notes / empty results	Index not built	Run `sync_index` once after install (and after bulk file changes)
Notes land in the wrong place	`SECOND_BRAIN_PATH` unset or wrong	Set it in your MCP config `env` block; defaults to `~/second-brain`
Tools unavailable when working in other project folders	Installed as local config instead of user scope	Re-register with `--scope user`: `claude mcp remove second-brain -s local && claude mcp add --scope user second-brain ...`

Vault Structure

vault/
├── 00-inbox/          # Unprocessed captures — clear daily
├── 10-projects/       # Active projects
├── 20-areas/
│   ├── research/      # Ongoing research domains
│   ├── coding/        # Dev tools and workflows
│   └── consolidated/  # Auto-merged similar notes (Phase 8)
├── 30-resources/      # ← Papers and articles (save_article writes here)
├── 40-archive/        # Compressed originals (auto-managed by vault_sleep)
├── decisions/         # Architecture Decision Records (ADR format)
├── memory/
│   ├── goals.md       # Current priorities — injected at every session start
│   ├── index.md       # Vault map
│   └── rules.md       # Auto-extracted L3 rules — injected at every session start
└── templates/         # Note templates (note, decision, project, research)

Running Tests

uv run pytest tests/ -v
uv run python benchmark.py --quick --markdown   # search latency + accuracy report

References & Acknowledgements

Papers That Directly Inspired This Project

Paper	Where Used
Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference (2026)	Phase 3 Vault Sleep — hippocampal replay as batch memory consolidation
Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents (2026)	Phase 9 adaptive tier — score × age dual-axis; addresses the "missing diagonal" in existing systems
DeepSeek-OCR: Contexts Optical Compression (2025)	Phase 4 PNG tiers — image as compressed medium, 10× compression at 97% fidelity
MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning (2026)	Phase 4 vision API — Playwright render → VLM reading pipeline
Active Context Compression: Autonomous Memory Management in LLM Agents (2026)	Phase 3 design comparison — session-level vs. nightly batch consolidation
SimpleMem: Efficient Lifelong Memory for LLM Agents (2026)	Phase 8 consolidation — 3-stage semantic compression, 30× token reduction
Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers (2026)	Architecture positioning — mechanisms, evaluation, and frontiers

Cognitive Science Foundations

Ebbinghaus, H. (1885). Über das Gedächtnis. — forgetting curve; basis for access_count / ln(age_days + 1)
Stickgold, R. (2005). Nature, 437, 1272–1278. — sleep-dependent memory consolidation

Built With

MarkItDown · DuckDB · llama.cpp · nomic-embed-text · FastMCP · Playwright · Anthropic Claude API

Contributing

PRs and Issues welcome. Please open an issue first to discuss significant changes.

second-brain-mcp