slipbox-mcp 1.3.0

A Zettelkasten knowledge management system as an MCP server

Slipbox MCP Server

Give your AI assistant an active role in managing your knowledge. Slipbox is an MCP server that turns any MCP-compatible agent into a Zettelkasten partner -- creating atomic notes, forming semantic links, detecting emergent clusters, and synthesizing insights from your existing knowledge.

Your ideas in, structured knowledge out. The agent handles the formatting, linking, and integration.

New to the method? Start with Introduction to the Zettelkasten Method for the why behind atomic notes and linked thinking.

Built and tested with Claude. Works with any MCP client (Claude Desktop, Claude Code, OpenCode, Copilot, or anything that speaks MCP).

Plain files, zero lock-in. Notes are markdown with YAML frontmatter -- readable in Obsidian, Foam, Logseq, or any editor. The SQLite database is an index, not the source of truth. Delete it and rebuild from files anytime.

• 19 MCP tools for notes, links, search, graph analysis, and cluster management
• 6 workflow prompts (plus matching skills) encoding the Zettelkasten method so you don't re-learn it every session
• BM25 full-text search across titles and content via SQLite FTS5
• Cluster detection finds emergent topic groups and scaffolds structure notes
• Seven typed links (reference, extends, refines, contradicts, questions, supports, related)

Python 3.10+ | macOS or Linux

In Action

Proactive Maintenance

The agent reads the slipbox://maintenance-status resource at session start and surfaces clusters that need organizing.

Full-Text Search

BM25-ranked search across notes via zk_search_notes.

Knowledge Graph: Central Notes

zk_find_central_notes surfaces the structural anchors of the graph -- the notes everything else orbits.

Direct Idea Capture

Your raw thinking in, a formatted atomic note with tags and links out. The agent formats and integrates -- the ideas stay yours.

Note Analysis

The analyze_note prompt evaluates atomicity, finds real connections in the existing graph, suggests tags, and rewrites for clarity.

Source Decomposition

The knowledge_creation prompt splits an article into atomic literature notes with proper citation and links.

Cluster Detection

zk_get_cluster_report finds groups of co-occurring tags that lack a structure note. Scored by size, orphan ratio, link density, and recency.

Structure Note Creation

zk_create_structure_from_cluster scaffolds a structure note, links all member notes, and dismisses the cluster.

Orphaned Notes

zk_find_orphaned_notes surfaces unintegrated knowledge -- candidates for connection or deletion.

Similar Notes

zk_find_similar_notes computes similarity from shared tags, common links, and content overlap.

Graph Traversal

zk_get_linked_notes shows typed links from a hub note, grouped by link type.

Knowledge Synthesis

The knowledge_synthesis prompt finds bridges between unconnected areas and proposes synthesis notes from your existing knowledge.

Zero Lock-In: Plain Files in Obsidian

Notes are plain markdown. Open the vault in Obsidian and everything works -- rendered content, backlinks, and the knowledge graph.

For a graph that renders the typed links in color (supports, extends, refines, ...) rather than Obsidian's untyped built-in graph, install the companion plugin Slipbox Semantic Graph -- a force-directed view with human-readable titles and color-coded semantic link types. Install it manually from the 0.1.0 release: copy main.js, manifest.json, and styles.css into <vault>/.obsidian/plugins/slipbox-graph/, then enable it in Settings → Community plugins. (Once it's accepted into the official directory, you'll also be able to install it via Settings → Community plugins → Browse → search "Slipbox Semantic Graph".) It reads the same frontmatter id and ## Links section the server writes, so no extra configuration is needed. Open the view with the Open semantic graph command (Command Palette) or the git-fork ribbon icon.

The legend across the top maps each color to a link type (extends, refines, supports, contradicts, questions, related). Focus a structure note and its constellation comes into view — here, Contract Testing Knowledge Map with its member notes orbiting it:

---

Quick Start

1. Clone and Install

git clone https://github.com/jamesfishwick/slipbox-mcp.git
cd slipbox-mcp

pipx install --editable .

pipx install puts a slipbox-mcp launcher on your PATH (in ~/.local/bin). That single command is the whole MCP server — no PYTHONPATH, no hardcoded venv Python path. Everything below uses it.

2. Pick a Data Directory

One variable, SLIPBOX_BASE_DIR, configures everything: notes land in <base>/data/notes and the SQLite index in <base>/data/db/zettelkasten.db. The server creates these on first run.

# Example — use any absolute path you like
/Users/yourname/.local/share/mcp/slipbox

Use a full absolute path. A leading ~ is not expanded inside MCP client config files and would create a literal ~ directory.

3. Connect to Your MCP Client

Claude Code — one command, no file editing:

claude mcp add slipbox \
  --env SLIPBOX_BASE_DIR=/Users/yourname/.local/share/mcp/slipbox \
  -- slipbox-mcp

Claude Desktop — edit the config file:

• macOS — ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux — ~/.config/claude/claude_desktop_config.json

{
  "mcpServers": {
    "slipbox": {
      "command": "slipbox-mcp",
      "env": {
        "SLIPBOX_BASE_DIR": "/Users/yourname/.local/share/mcp/slipbox"
      }
    }
  }
}

Desktop PATH caveat: the macOS Desktop app doesn't always inherit ~/.local/bin on its PATH, so the bare "slipbox-mcp" may not resolve. If the server fails to start, replace "command": "slipbox-mcp" with the absolute path printed by which slipbox-mcp (typically /Users/yourname/.local/bin/slipbox-mcp).

Other MCP clients: register slipbox-mcp as the server command with SLIPBOX_BASE_DIR in its environment. The command and env are the same everywhere.

Advanced: split notes and database across separate locations

Instead of SLIPBOX_BASE_DIR, set absolute paths individually. Optional SLIPBOX_LOG_LEVEL is one of DEBUG, INFO, WARNING, ERROR.

"env": {
  "SLIPBOX_NOTES_DIR": "/Users/yourname/.local/share/mcp/slipbox/notes",
  "SLIPBOX_DATABASE_PATH": "/Users/yourname/.local/share/mcp/slipbox/data/db/zettelkasten.db",
  "SLIPBOX_LOG_LEVEL": "INFO"
}

4. Restart and Verify

Restart your client (Claude Code reloads on next launch; quit and reopen Claude Desktop).

Ask your agent:

• "Create a test note about something"
• "Search my slipbox for test"
• "Find orphaned notes"

---

Optional: Automatic Cluster Detection

Cluster analysis scans all notes and computes similarity scores. Running it daily (6am) pre-computes results so slipbox_get_cluster_report() returns instantly. Without scheduling, cluster detection runs on-demand, which is slower for large collections.

Run manually after bulk imports, major reorganization, or when you want immediate results.

Install Cluster Detection (macOS)

chmod +x scripts/install-cluster-detection.sh
./scripts/install-cluster-detection.sh

The installer detects your Python/venv path, generates the LaunchAgent plist, and loads it.

Manual Test (File Watcher)

source .venv/bin/activate
python scripts/detect_clusters.py

Output saved to ~/.local/share/mcp/slipbox/cluster-analysis.json.

Uninstall Cluster Detection

./scripts/install-cluster-detection.sh --uninstall

---

Optional: macOS File Watcher for Auto-Indexing

The MCP server maintains a database index for fast searching. Editing notes in Obsidian (or any editor) makes the database stale until you run slipbox_rebuild_index.

The file watcher runs as a background daemon, monitoring your notes directory and automatically rebuilding the index when .md files change.

Use it if you frequently edit notes in Obsidian while also using Claude.

Install File Watcher (macOS)

chmod +x scripts/install-file-watcher.sh
./scripts/install-file-watcher.sh

The installer detects your Python/venv path, installs watchdog if needed, and loads the LaunchAgent. Starts on login and restarts if it crashes.

Manual Test

source .venv/bin/activate
python scripts/watch_notes.py

Edit a note file—you should see "rebuilding index..." in the watcher output.

Check Status

launchctl list | grep slipbox.watcher

# View logs

tail -f ~/.local/share/mcp/slipbox/watcher.log

Uninstall File Watcher

./scripts/install-file-watcher.sh --uninstall

---

Recommended System Prompt

Add the system prompt from docs/SYSTEM_PROMPT.md to your agent's preferences or system prompt. This enables:

• Automatic knowledge capture during conversations
• Cluster emergence detection at conversation start
• Proper Zettelkasten workflows (search before create, link immediately)

---

Tools Reference

Core Note Operations

Tool	Description
slipbox_create_note	Create atomic notes (fleeting/literature/permanent/structure/hub)
slipbox_get_note	Retrieve note by ID or title
slipbox_update_note	Update existing notes
slipbox_delete_note	Delete notes

Linking

Tool	Description
slipbox_create_link	Create semantic links between notes
slipbox_remove_link	Remove links
slipbox_delete_link	Delete a specific link (errors if link does not exist)
slipbox_get_linked_notes	Get notes linked to/from a note

Search & Discovery

Tool	Description
slipbox_search_notes	Search by text (BM25-ranked), tags, or type
slipbox_find_similar_notes	Find notes similar to a given note
slipbox_find_central_notes	Find most connected notes
slipbox_find_orphaned_notes	Find unconnected notes
slipbox_list_notes_by_date	List notes by date range
slipbox_get_all_tags	List all tags

Cluster Analysis

Tool	Description
slipbox_get_cluster_report	Get pending clusters needing structure notes
slipbox_create_structure_from_cluster	Create structure note from cluster
slipbox_refresh_clusters	Regenerate cluster analysis
slipbox_dismiss_cluster	Permanently dismiss cluster from suggestions

Maintenance

Tool	Description
slipbox_rebuild_index	Rebuild database index from files

---

Prompts Reference

MCP prompts are reusable workflow templates that encode the Zettelkasten method so you don't re-explain it every session.

Prompt	Description	Use When
knowledge_creation	Process information into 3-5 atomic notes	Adding articles, ideas, or notes
knowledge_creation_batch	Process larger volumes into 5-10 notes	Processing books or long-form content
knowledge_exploration	Map connections to existing knowledge	Exploring how topics relate
knowledge_synthesis	Create higher-order insights	Finding bridges between ideas
analyze_note	Evaluate a note's fitness for the slipbox	Reviewing a new or existing note
cluster_maintenance	Surface pending housekeeping	Start of a working session

How to Invoke: Slash Commands and Skills

Each workflow ships two ways:

• MCP prompts — served by the running server.
• Skills — standalone bundles (skills/<name>/) that run the same workflow and add natural-language triggering.

Five of the six skills are generated from the same PROMPT_* templates the server uses (src/slipbox_mcp/server/descriptions.py), and CI fails if the committed skills/ drift from those templates. The sixth, cluster-maintenance, is authored directly in scripts/build_skills.py because its MCP prompt is a runtime-rendered status message rather than a reusable workflow.

Slash commands are the reliable path. Claude Code surfaces MCP prompts as /mcp__<server>__<prompt>; type /mcp__slipbox-mcp__ for the picker:

/mcp__slipbox-mcp__knowledge_creation
/mcp__slipbox-mcp__knowledge_exploration
/mcp__slipbox-mcp__knowledge_synthesis
/mcp__slipbox-mcp__knowledge_creation_batch
/mcp__slipbox-mcp__analyze_note
/mcp__slipbox-mcp__cluster_maintenance

(Installed skills also expose their own slash commands by directory name, e.g. /slipbox-analyze-note.)

Natural language works once the matching skill is installed — just describe what you want:

Analyze this note for my slipbox: [paste note]

Add this to my slipbox: [paste article]

Synthesize my notes on attention and memory.

Prose triggering depends on the skill being installed and your phrasing matching its description; fall back to the slash command if it doesn't fire. Asking the model to "use the analyze_note prompt" by name does not work — the model can't invoke an MCP prompt by name. Use a slash command, or let a skill trigger from natural language.

Installing Skills

Claude Code discovers skills from .claude/skills/ (per project) or ~/.claude/skills/ (global), not from a bare top-level skills/. Symlink or copy the ones you want into a discovery path — e.g. for this project:

mkdir -p .claude/skills
ln -s ../../skills/slipbox-analyze-note .claude/skills/slipbox-analyze-note
# ...or copy the directories, or symlink all six

Claude Desktop needs each skill as a .skill bundle. Build them, then upload:

python scripts/build_skills.py     # writes dist/*.skill

Go to Settings → Skills → Upload skill and select the bundles from dist/ you want. Each installs as both a slash command and a natural-language trigger.

After editing a prompt template in descriptions.py, re-run the build to regenerate the skills.

---

Link Types

Type	Use When	Inverse
reference	Generic "see also" connection	reference
extends	Building on another idea	extended_by
refines	Clarifying or improving	refined_by
contradicts	Opposing view	contradicted_by
questions	Raising questions about	questioned_by
supports	Providing evidence for	supported_by
related	Loose thematic connection	related

---

Note Types

Type	Purpose
fleeting	Quick captures, unprocessed thoughts
literature	Ideas from sources with citation
permanent	Refined ideas in your own words
structure	Maps organizing 7-15 related notes on a specific topic
hub	Domain overview linking to structure notes; entry point for navigating a broad area of knowledge

Structure vs. Hub: A structure note organizes a cluster of permanent notes around a single topic — it is a curated map one level above the notes themselves. A hub note operates one level higher still: it links to structure notes (and occasionally key permanent notes) across an entire knowledge domain. Where a structure note answers "what do I know about X?", a hub note answers "how is my knowledge of this whole domain organized?" Most Zettelkastens need only a handful of hub notes.

---

File Format

Notes are stored as Markdown files with YAML frontmatter:

---
id: "20251217T172432480464000"
title: "Poetry Revision Principles"
type: structure
tags:
  - poetry
  - revision
  - craft
created: "2025-12-17T17:24:32"
updated: "2025-12-17T17:24:32"
---

# Poetry Revision Principles

Content here...

## Links

- reference [[20250728T125429845760000]] Member of structure

You can edit these files directly in any text editor or Obsidian. Run slipbox_rebuild_index after external edits.

---

Upgrading

After pulling new versions, restart Claude Desktop. If the release notes mention database changes, run slipbox_rebuild_index once to bring your existing database up to date.

Upgrading to FTS5 search (any version after the FTS5 release): The full-text search index is created automatically when the server starts against a new database. For existing databases, the FTS5 table will be created on first startup but will be empty until you run:

slipbox_rebuild_index

This populates the BM25 index from your existing notes. Search results will not be relevance-ranked until this is done.

---

Troubleshooting

Server not loading in Claude Desktop

1. Confirm the launcher resolves: which slipbox-mcp should print a path (typically ~/.local/bin/slipbox-mcp).
2. If it resolves in your terminal but Desktop still can't start it, the GUI app isn't seeing ~/.local/bin on its PATH. Replace "command": "slipbox-mcp" with the absolute path from step 1.
3. Check Claude Desktop logs for errors.

slipbox-mcp: command not found

The console script wasn't installed or isn't on PATH. Reinstall with pipx install --editable . --force, then verify with which slipbox-mcp. If pipx's bin directory is missing from PATH, run pipx ensurepath and restart your shell.

Notes directory points to ~/... literally

If your notes directory ends up at ./~/... relative to CWD, you used ~ in the JSON config. Claude Desktop does not expand ~. Replace it with the full absolute path.

Search returns no results

1. The FTS5 index may not be populated. Run slipbox_rebuild_index once to index existing notes.
2. If you recently edited notes outside Claude, the index may be stale. Run slipbox_rebuild_index.

slipbox_list_notes_by_date returns empty results

If start_date is later than end_date, no notes match and an empty result is returned — this is expected behavior, not an error.

Database out of sync

If notes were edited outside the MCP server:

slipbox_rebuild_index

Cluster detection not running

launchctl list | grep slipbox.cluster-detection
# Should show: - 0 com.slipbox.cluster-detection

# Check logs

cat /tmp/slipbox-clusters.log

# Reinstall if needed

./scripts/install-cluster-detection.sh --uninstall
./scripts/install-cluster-detection.sh

File watcher not running

launchctl list | grep slipbox.watcher
# Should show: - 0 com.slipbox.watcher

# Check logs

cat ~/.local/share/mcp/slipbox/watcher.log

# Reinstall if needed

./scripts/install-file-watcher.sh --uninstall
./scripts/install-file-watcher.sh

Upgrading from ZETTELKASTEN_* environment variables

If you previously used ZETTELKASTEN_NOTES_DIR, ZETTELKASTEN_DATABASE_PATH, or other ZETTELKASTEN_* variables, they are no longer read. Rename them to their SLIPBOX_* equivalents:

Old	New
ZETTELKASTEN_NOTES_DIR	SLIPBOX_NOTES_DIR
ZETTELKASTEN_DATABASE_PATH	SLIPBOX_DATABASE_PATH
ZETTELKASTEN_LOG_LEVEL	SLIPBOX_LOG_LEVEL
ZETTELKASTEN_BASE_DIR	SLIPBOX_BASE_DIR
ZETTELKASTEN_SERVER_NAME	SLIPBOX_SERVER_NAME

The server logs a warning if old names are detected, but does not migrate them automatically.

Cluster report path is not configurable

The cluster analysis report always writes to ~/.local/share/mcp/slipbox/cluster-analysis.json, regardless of SLIPBOX_BASE_DIR or SLIPBOX_NOTES_DIR. If you use non-default paths, the cluster report will still be in the default location.

Install scripts are macOS-only

The scripts/install-cluster-detection.sh and scripts/install-file-watcher.sh scripts use launchctl and ~/Library/LaunchAgents/, which only exist on macOS. On Linux, you'll need to create equivalent systemd units or cron jobs manually. See the manual test commands in the relevant README sections to verify the underlying Python scripts work on your platform.

Default paths are relative to the working directory

If SLIPBOX_NOTES_DIR and SLIPBOX_DATABASE_PATH are not set, the server defaults to data/notes and data/db/zettelkasten.db relative to the current working directory. When running via Claude Desktop, the CWD may not be what you expect. Always set absolute paths in claude_desktop_config.json to avoid this.

---

Development

Setup

git clone https://github.com/jamesfishwick/slipbox-mcp.git
cd slipbox-mcp
uv venv && uv pip install -e ".[dev]"

Testing

The project has three tiers of tests:

Tier	Count	Speed	Cost	Command
Unit + integration	219	~2s	Free	pytest tests/
Tool contract tests	22	~0.5s	Free	pytest evals/tool_contracts/
LLM evals	28	~10min	~$3-5	pytest evals/llm/

# Default: runs unit + contract tests (CI runs this)

pytest

# Run everything except LLM evals

pytest tests/ evals/tool_contracts/

# Run LLM evals (requires claude CLI authenticated)

pytest evals/llm/ -v

# Run LLM evals with a specific model

EVAL_MODEL=sonnet pytest evals/llm/ -v

# Lint

ruff check src/ evals/

Unit tests cover internal logic -- services, repository, models, parsing.

Tool contract tests verify the MCP tool output format that the LLM sees -- parseable structure, chaining (create -> search -> get), and helpful error messages. These are deterministic and don't call any LLM.

LLM evals send prompts to an LLM via the claude CLI with the MCP server connected, then grade results by inspecting the database state (notes created, links made, tags applied). They test whether the LLM actually uses the tools correctly given the tool descriptions.

CI/CD

Branch protection: Direct pushes to main are blocked. All changes go through PRs.

Workflow	Trigger	Runner	What
CI	Every PR + push to main	GitHub-hosted	Unit + contract tests, ruff
LLM Evals	PRs changing prompt files	Self-hosted	28 LLM evals via claude CLI
Release	Push of a v*.*.* tag	GitHub-hosted	Build, verify, publish to PyPI

The LLM eval workflow triggers only when these files change:

• src/slipbox_mcp/server/descriptions.py (tool descriptions)
• src/slipbox_mcp/server/prompts.py (prompt templates)
• evals/llm/**, evals/seed_data.py, evals/conftest.py

Customizing the eval setup

If you don't want a self-hosted runner: Remove .github/workflows/llm-evals.yml and run LLM evals locally before merging prompt changes:

pytest evals/llm/ -v

If you want LLM evals on every PR (not just prompt changes): Edit .github/workflows/llm-evals.yml and remove the paths: filter.

To change the default eval model: Set EVAL_MODEL in your environment or in the workflow file. Default is haiku for speed/cost.

To set up a self-hosted runner:

# Get a registration token

gh api repos/OWNER/REPO/actions/runners/registration-token -X POST -q '.token'

# Download and configure

mkdir -p ~/.github-runners/slipbox-mcp && cd ~/.github-runners/slipbox-mcp
curl -sL -o actions-runner.tar.gz https://github.com/actions/runner/releases/latest/download/actions-runner-osx-arm64-2.325.0.tar.gz
tar xzf actions-runner.tar.gz
./config.sh --url https://github.com/OWNER/REPO --token <TOKEN> --unattended
nohup ./run.sh &

Publishing to PyPI

Releases go out via the Release workflow (.github/workflows/release.yml) using PyPI Trusted Publishing — OIDC, so no API token is stored in repo secrets.

One-time setup (before the first release):

1. On PyPI, register a pending trusted publisher for the project name slipbox-mcp:
   • Owner: jamesfishwick · Repository: slipbox-mcp
   • Workflow: release.yml · Environment: release
2. In the GitHub repo, create an environment named release (Settings → Environments). Optionally restrict it to tags matching v*.

Cut a release:

# 1. Bump __version__ in src/slipbox_mcp/__init__.py (the single source of truth --
#    pyproject.toml reads it dynamically via [tool.setuptools.dynamic]).
# 2. Commit the bump via PR and merge to main, then tag the merge commit:

git tag v1.2.2
git push origin v1.2.2

The workflow verifies the tag matches the package version, builds the sdist + wheel, runs twine check, and publishes to PyPI. To rehearse without publishing for real, build and upload to TestPyPI by hand first — python -m build && twine upload --repository testpypi dist/* (needs a TestPyPI token); the tagged workflow itself only targets production PyPI.

The version is defined once, in src/slipbox_mcp/__init__.py. pyproject.toml (dynamic = ["version"]) and the MCP server's reported server_version both read from it, so there is nothing to keep in sync.

Shared prompt constants

All tool descriptions and prompt templates live in src/slipbox_mcp/server/descriptions.py. Both the MCP server and the eval tests import from this single source of truth. If you change a prompt, the evals test whether the LLM still behaves correctly with the new wording.

Debug logging

SLIPBOX_LOG_LEVEL=DEBUG python -c "from slipbox_mcp.main import main; main()"

---

CLI Tool

The slipbox command provides terminal access for mechanical operations:

slipbox status          # Overview of notes, tags, orphans, pending clusters
slipbox search <query>  # Find notes by text
slipbox clusters        # Show pending structure note candidates
slipbox orphans         # List unconnected notes
slipbox rebuild         # Rebuild index (add --clusters to refresh cluster analysis)
slipbox export <id>     # Export note markdown to stdout
slipbox tags            # List all tags with usage counts

Install: pipx install --editable . (adds slipbox to your PATH)

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Contributing

See CONTRIBUTING.md for setup instructions, coding standards, and how to submit changes.

Roadmap

See ROADMAP.md for planned features and future direction.

Sponsor

If slipbox-mcp is useful to you, consider sponsoring the project.

License

MIT