Slipbox MCP Server
Slipbox MCP Server gives an AI assistant a full Zettelkasten knowledge-management toolkit: create and organize atomic notes, link them semantically, search and explore the graph, and maintain structure through automatic cluster detection.
Note management: Create, read, update, and delete notes as markdown files with YAML frontmatter, supporting five types (fleeting, literature, permanent, structure, hub).
Semantic linking: Create and remove typed directional links (reference, extends, refines, contradicts, questions, supports, related), optionally bidirectional with inverse links.
Graph exploration: View linked notes, find central/hub-worthy notes, find orphaned/unintegrated notes, and discover similar notes via shared tags, links, and content overlap.
Search & discovery: Full-text BM25 search by query, tags, or note type; list notes by date range; list all tags for consistent tagging.
Cluster analysis & maintenance: Get pending cluster reports, create structure notes from detected clusters, refresh cluster analysis, and dismiss clusters you don't need.
Indexing & portability: Rebuild the SQLite index from plain markdown files after external edits, ensuring zero lock-in and file-based source of truth.
Workflow support: Ship with Zettelkasten prompts/skills for knowledge creation, exploration, synthesis, note analysis, and cluster maintenance to guide the agent's workflow.
Allows notes to be managed as plain markdown files viewable in Obsidian, and provides a companion plugin for visualizing typed links in a color-coded semantic graph within Obsidian.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Slipbox MCP Serversearch for notes on complex adaptive systems"
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.
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. To see how Slipbox primes your agent with that method, read the server instructions it ships automatically on connect.
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

Walkthrough
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Related MCP server: vault-master-mcp
Quick Start
1. Install
pipx install slipbox-mcp
# or, with uv:
uv tool install slipbox-mcpThis puts a slipbox-mcp launcher on your PATH (in ~/.local/bin). That single command is the whole MCP server: no clone, no PYTHONPATH, no hardcoded venv Python path. Everything below uses it. To try it without installing at all, uvx slipbox-mcp runs the server in a throwaway environment.
(Working on Slipbox itself? See Development for the clone + editable-install setup.)
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 with owner-only (0700) permissions.
Point SLIPBOX_BASE_DIR (or the individual SLIPBOX_NOTES_DIR / SLIPBOX_DATABASE_PATH paths below) at a dedicated data directory you control, not a shared or system location. These paths are used as-is: the server manages the notes tree and index under them, and it treats the notes directory as the source of truth when it rebuilds the index.
# Example: use any absolute path you like
/Users/yourname/.local/share/mcp/slipboxUse 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-mcpClaude Desktop (edit the config file):
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonLinux:
~/.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/binon 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 bywhich 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.
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"
}The cluster report is written beside the database as cluster-analysis.json. Set SLIPBOX_CLUSTER_REPORT_PATH to an absolute path to put it elsewhere.
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"
In Action
The hero above is the core loop. Here's the rest of what the agent does.
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 slipbox_search_notes.

Knowledge Graph: Central Notes
slipbox_find_central_notes surfaces the structural anchors of the graph -- the notes everything else orbits.

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

Orphaned Notes
slipbox_find_orphaned_notes surfaces unintegrated knowledge -- candidates for connection or deletion.

Similar Notes
slipbox_find_similar_notes computes similarity from shared tags, common links, and content overlap.

Graph Traversal
slipbox_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:

Optional: Automatic Cluster Detection
The background helpers below require a repository checkout and its own Python environment; pipx install and uv tool install install the server but do not put these scripts in your working directory. Set up the helpers once:
git clone https://github.com/jamesfishwick/slipbox-mcp.git
cd slipbox-mcp
uv sync
cp .env.example .envEdit the checkout's .env to use the same absolute data paths as your MCP client. For example, for a client whose SLIPBOX_BASE_DIR is /Users/yourname/.local/share/mcp/slipbox:
SLIPBOX_BASE_DIR=/Users/yourname/.local/share/mcp/slipbox
SLIPBOX_NOTES_DIR=/Users/yourname/.local/share/mcp/slipbox/data/notes
SLIPBOX_DATABASE_PATH=/Users/yourname/.local/share/mcp/slipbox/data/db/zettelkasten.db
SLIPBOX_LOG_LEVEL=INFORun the following commands from this checkout. Keep it in place while the background jobs are installed: their LaunchAgents use its absolute paths.
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.shThe installer detects your Python/venv path, generates the LaunchAgent plist, and loads it.
Manual Test (Cluster Detection)
source .venv/bin/activate
python scripts/detect_clusters.pyOutput saved to cluster-analysis.json next to the SQLite index (for example <base>/data/db/cluster-analysis.json), or to SLIPBOX_CLUSTER_REPORT_PATH when set.
Uninstall Cluster Detection
./scripts/install-cluster-detection.sh --uninstallOptional: 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)
Complete the helper setup above first, and create a note through your MCP client so the notes directory exists.
chmod +x scripts/install-file-watcher.sh
./scripts/install-file-watcher.shThe 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.pyEdit 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.logUninstall File Watcher
./scripts/install-file-watcher.sh --uninstallRecommended System Prompt
Slipbox ships a baseline automatically: every client receives the server instructions on connect, covering how to use the tools well -- note types, link semantics, quality standards, and core workflows like search-before-create. You don't add those yourself.
docs/SYSTEM_PROMPT.md is the opt-in layer on top: the autonomy and initiative directives a server shouldn't assert on its own. Add it to your agent's preferences or system prompt to enable:
Automatic knowledge capture during conversations
Cluster emergence detection at conversation start
Tools Reference
Core Note Operations
Tool | Description |
| Create atomic notes (fleeting/literature/permanent/structure/hub) |
| Retrieve note by ID or title |
| Update existing notes |
| Delete notes |
Linking
Tool | Description |
| Create semantic links between notes |
| Remove links |
| Delete a specific link (errors if link does not exist) |
| Get notes linked to/from a note |
Search & Discovery
Tool | Description |
| Search by text (BM25-ranked), tags, or type |
| Find notes similar to a given note |
| Find most connected notes |
| Find unconnected notes |
| List notes by date range |
| List all tags |
Cluster Analysis
Tool | Description |
| Get pending clusters needing structure notes |
| Create structure note from cluster |
| Regenerate cluster analysis |
| Permanently dismiss cluster from suggestions |
Maintenance
Tool | Description |
| 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 |
| Process information into 3-5 atomic notes | Adding articles, ideas, or notes |
| Process larger volumes into 5-10 notes | Processing books or long-form content |
| Map connections to existing knowledge | Exploring how topics relate |
| Create higher-order insights | Finding bridges between ideas |
| Evaluate a note's fitness for the slipbox | Reviewing a new or existing note |
| 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 sixClaude Desktop needs each skill as a .skill bundle. Build them, then upload:
python scripts/build_skills.py # writes dist/*.skillGo 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 |
| Generic "see also" connection | reference |
| Building on another idea | extended_by |
| Clarifying or improving | refined_by |
| Opposing view | contradicted_by |
| Raising questions about | questioned_by |
| Providing evidence for | supported_by |
| Loose thematic connection | related |
Note Types
Type | Purpose |
| Quick captures, unprocessed thoughts |
| Ideas from sources with citation |
| Refined ideas in your own words |
| Maps organizing 7-15 related notes on a specific topic |
| 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 structureYou 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_indexThis 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
Confirm the launcher resolves:
which slipbox-mcpshould print a path (typically~/.local/bin/slipbox-mcp).If it resolves in your terminal but Desktop still can't start it, the GUI app isn't seeing
~/.local/binon its PATH. Replace"command": "slipbox-mcp"with the absolute path from step 1.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
The FTS5 index may not be populated. Run
slipbox_rebuild_indexonce to index existing notes.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_indexCluster 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.shFile 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.shUpgrading 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 |
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The server logs a warning if old names are detected, but does not migrate them automatically.
Cluster report moved next to each vault's index
Older releases wrote one cluster report for every vault on the machine, at ~/.local/share/mcp/slipbox/cluster-analysis.json. Each vault now keeps its own cluster-analysis.json beside its SQLite index, so separate vaults no longer share clusters or dismissals. Set SLIPBOX_CLUSTER_REPORT_PATH to put it somewhere else.
The first time a vault has no report of its own, it checks the old shared file. It adopts that report, dismissals included, only if most of the notes listed in its clusters exist in the vault. Otherwise the vault starts fresh. The old file is never modified or deleted, so you can remove it yourself once each vault has its own report.
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 |
|
Tool contract tests | 22 | ~0.5s | Free |
|
LLM evals | 28 | ~10min | ~$3-5 |
|
# 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 |
| Every PR + push to main | GitHub-hosted | Unit + contract tests, ruff lint + format |
| Opt-in (label or manual) | Self-hosted | 28 LLM evals via claude CLI |
| Push to | GitHub-hosted | release-please PR; on its merge, build + publish to PyPI |
The LLM eval suite is expensive (~$3-5, ~10 min) and self-hosted, so it never runs automatically. A path-based trigger can't distinguish a real prompt change from a cosmetic reformat. Run it deliberately when you change prompt or tool-description semantics:
Add the
run-llm-evalslabel to the PR. It runs, and re-runs on each push while the label is present.Or trigger it manually from the Actions tab (
workflow_dispatch).Or run it locally without the runner:
pytest evals/llm/ -v.
Without a label or manual dispatch, the job is skipped (no runner allocated, no cost).
Customizing the eval setup
If you don't want a self-hosted runner: remove .github/workflows/llm-evals.yml and run pytest evals/llm/ -v locally before merging prompt changes.
If you want LLM evals on every PR automatically: add a pull_request trigger with the relevant paths: filter and drop the label gate in the job's if:. But expect incidental triggers from formatting-only edits.
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 &Releasing to PyPI
Releases are automated. The Release workflow (.github/workflows/release.yml) runs release-please on every push to main and publishes via PyPI Trusted Publishing (OIDC, so no API token is stored in repo secrets).
The flow (you never hand-edit a version or push a tag):
Land changes on
mainwith Conventional Commit messages (feat:→ minor bump,fix:→ patch,feat!:/BREAKING CHANGE:→ major). The repo's commit hooks already enforce this shape.release-please keeps a standing "release PR" open, accumulating the next version bump (in
src/slipbox_mcp/__init__.py) and theCHANGELOG.mdentries derived from those commits.When you're ready to ship, merge the release PR. That tags the release (
v<version>) and, in the same workflow run, builds the sdist + wheel, runstwine check, and publishes to PyPI.
So cutting a release is one click: merge the bot's PR. Nothing else.
Commit types decide the version. So type accurately. The bump is computed mechanically from the Conventional Commit prefixes since the last release, not from the size of the change. Reserve feat:/fix: for changes to the shipped package; use the non-releasing types for everything else:
Prefix | Version effect | Use for |
| minor (1.3.0 → 1.4.0) | new runtime capability in the package |
| patch (1.3.0 → 1.3.1) | bug fix in the package |
| major (1.3.0 → 2.0.0) | backwards-incompatible change |
| none | docs, tooling, CI, packaging, internal-only changes |
A batch of only non-releasing commits produces no release PR at all. The squash-merge title is the commit release-please reads, so the PR title's prefix is what counts. Label it for what the package gains, not for the effort spent.
One-time setup (already done for this repo, documented for forks):
On PyPI, register a pending trusted publisher for project
slipbox-mcp: Owner:jamesfishwick· Repository:slipbox-mcp· Workflow:release.yml· Environment:release. All four must match exactly.In GitHub, create an environment named
release(Settings → Environments). If you restrict its deployment refs, add a tag rulev*(a branch rule of the same name will not match the tag).
The version is defined once, in
src/slipbox_mcp/__init__.py(release-please bumps it; the# x-release-please-versionmarker tells it which line).pyproject.toml(dynamic = ["version"]) and the server'sserver_versionboth read from it, so there is nothing to keep in sync; the tag release-please cuts always matches the package version by construction.
To rehearse a build without publishing, run it by hand: python -m build && twine check dist/* (and twine upload --repository testpypi dist/* with a TestPyPI token to dry-run the upload).
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 countsInstall: pipx install --editable . (adds slipbox to your PATH)
Experimental: Slipbox as agent memory
An untested hypothesis, not a recommended setup. Everything above helps an agent manage your knowledge. This inverts it: the agent uses a slipbox as its own persistent memory across sessions, in place of native memory or a rules file.
The model has no memory between sessions, so the slipbox is the only channel one session leaves for the next. It writes briefings for a cold successor (a failure and why, a recurring constraint, a correction, a hard-won fact), tags them agent-memory, and searches that tag before acting. The bet is that a connected memory beats a flat rules file, because you retrieve it by traversal.
Three things to know first: namespace isolation is a tag convention, not enforced, so run it against a separate slipbox instance; "memory" is a misnomer, since nothing persists but the notes themselves; and the growth discipline is the unproven part, so expect sprawl on the first run. Full write-up and caveats: Slipbox as Agent Self-Memory.
Documentation
Doc | What's in it |
Note ID format, the five note types, and a one-page cheat sheet for the method. | |
Running the same workflow by hand in Obsidian, no agent involved. | |
How Slipbox's links map to | |
Which other tools can read and write the same vault. | |
The opt-in autonomy layer: auto-capture, cluster detection, and the agent-memory experiment. | |
A worked session showing the tools in use. |
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.
Acknowledgments
Slipbox started as a fork of zettelkasten-mcp by Peter J. Herrel (@diggy) and Entanglr. It has since been largely rewritten and extended, but its core data model still comes from that project: the note types, typed links and their inverses, and timestamp-based note IDs. Thanks to them for the foundation.
License
MIT
Available Tools
19 toolsslipbox_create_linkA
Create a semantic link between two notes.
Links are directional: source -> target. Use bidirectional=true for important relationships (automatically creates inverse link type).
Link Types:
reference: Generic "see also" connection
extends: Source builds upon target (inverse: extended_by)
refines: Source clarifies or improves target (inverse: refined_by)
contradicts: Source presents opposing view (inverse: contradicted_by)
questions: Source raises questions about target (inverse: questioned_by)
supports: Source provides evidence for target (inverse: supported_by)
related: Loose thematic connection (symmetric)
Best Practices:
Always add description explaining WHY notes are linked
Use bidirectional=true for substantive relationships
Create links immediately after creating notes
Args: source_id: ID of the source note (the note doing the linking) target_id: ID of the target note (the note being linked to) link_type: One of reference/extends/refines/contradicts/questions/supports/related description: Brief explanation of the relationship bidirectional: If true, creates inverse link from target to source
| Name | Required | Description | Default |
|---|---|---|---|
| link_type | No | reference | |
| source_id | Yes | ||
| target_id | Yes | ||
| description | No | ||
| bidirectional | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains directionality (source -> target), the automatic inverse link when bidirectional=true, and enumerates all link types with their inverses. This covers core behavior well, though it omits details about error handling or note existence validation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for purpose, link types, best practices, and args. It is fairly long but every sentence is informative and avoids redundancy, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
It covers all essential details for correct invocation: direction, link types, bidirectional behavior, parameter meanings, and best practices. The existence of an output schema means return values need not be explained. It lacks edge-case details (e.g., duplicate links), but that is not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (schema only gives types and defaults), but the description fully compensates by explaining each parameter, including the valid values for link_type and the meaning of bidirectional. This adds significant semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: creating a semantic link between two notes. It distinguishes itself from sibling tools like slipbox_delete_link and slipbox_get_linked_notes by focusing on the create operation, and it adds nuance with directionality and link type semantics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides best practices (e.g., 'Create links immediately after creating notes') and explains when to use bidirectional=true, giving clear contextual guidance. However, it does not explicitly compare to alternatives like slipbox_remove_link or slipbox_get_linked_notes, which is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_create_noteA
Create a new atomic Zettelkasten note.
Each note should contain exactly one idea. After creating, immediately
link to related notes using slipbox_create_link.
Note Types:
- fleeting: Quick captures, unprocessed thoughts (process within 24-48 hours)
- literature: Ideas extracted from sources. REQUIRES at least one entry
in references (citation or URL). If you don't yet have the citation,
use 'fleeting' as a staging type and promote to 'literature' once
the source is attached.
- permanent: Refined ideas in the user's own words (the core of your Zettelkasten)
- structure: Maps organizing 7-15 related notes on a topic
- hub: Entry points into major knowledge domains
Authoring:
- Store `content` exactly as the user provided it. Do NOT rewrite,
expand, or restructure their words. Compose or refine content only
when explicitly asked (e.g. "draft", "refine", "make it standalone").
Best Practices:
- Title should express the idea in brief (understandable without reading content)
- A refined permanent note typically runs 3-7 paragraphs and stands
alone; match the length of what the user gave you, though — a
one-line capture stays one line.
- Use 2-5 specific tags; prefer existing tags when they fit
- Search first (slipbox_search_notes) to avoid duplicating existing notes
Args:
title: Concise title expressing the core idea
content: Full note content in markdown
note_type: One of fleeting/literature/permanent/structure/hub (default: permanent)
tags: Comma-separated tags, e.g. "poetry,revision,craft"
references: Newline-separated citations to external sources (e.g. "Ahrens, S. (2017). How to Take Smart Notes.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| title | Yes | ||
| content | Yes | ||
| note_type | No | permanent | |
| references | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden and excels: it mandates exact content preservation ('Store content exactly as the user provided it'), explains note type behavior, requires references for literature notes, and warns against rewriting unless explicitly asked. This goes far beyond what the schema alone conveys.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but appropriately structured into labeled sections (Note Types, Authoring, Best Practices, Args), with every section adding practical guidance. The core purpose is front-loaded in the first sentence, and the Args section maps cleanly to the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex creation tool with no annotations, the description covers note type semantics, staging workflow, content fidelity, tagging, duplicate avoidance, linking, and parameter formats. An agent has enough context to invoke it correctly without needing return-value details, especially since an output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only source of parameter meaning. It defines title, content, note_type, tags, and references with formats, examples, defaults, and type-specific constraints, fully compensating for the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Create a new atomic Zettelkasten note,' a specific verb, resource, and core constraint. It clearly differentiates itself from siblings like slipbox_create_link and slipbox_update_note by focusing on note creation and atomicity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance: search first with slipbox_search_notes to avoid duplicates, create links immediately after with slipbox_create_link, and use 'fleeting' as a staging type when citations are missing. These are concrete when/when-not and alternative-tool instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_create_structure_from_clusterA
Create a structure note from a detected cluster.
Generates a structure note organizing all notes in the cluster, with bidirectional links to each member note.
Run slipbox_get_cluster_report first to see available clusters and their IDs.
Args: cluster_id: ID from cluster report (e.g. "jackson-mac-low-chance-operations") title: Override the suggested title (optional) create_links: Create bidirectional links to member notes (default: true)
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | ||
| cluster_id | Yes | ||
| create_links | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the behavioral burden. It discloses that the tool generates a structure note, organizes all member notes, and creates bidirectional links, and it notes the default create_links behavior in Args. It does not discuss failure modes or side effects on the cluster state, but for a creation operation the core behavior is transparent enough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The purpose is front-loaded, the prerequisite is in a short sentence, and the Args section is scannable. There is minor redundancy between the opening sentence and 'Generates a structure note...', but the added detail about bidirectional links earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the agent does not need the description to explain return values. The description covers the prerequisite, the behavior, and all parameters, which is sufficient for a 3-parameter creation tool with no nested objects. It does not address potential errors like invalid cluster IDs, but that is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain all parameters. It does: cluster_id is defined as an ID from the cluster report with a concrete example, title is an optional override of the suggested title, and create_links is explained with its default true. This fully compensates for the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Create a structure note from a detected cluster.' It further clarifies that it organizes all notes in the cluster and creates bidirectional links, which distinguishes it from generic slipbox_create_note and from cluster-management siblings like slipbox_dismiss_cluster.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a concrete prerequisite: 'Run slipbox_get_cluster_report first to see available clusters and their IDs.' This tells an agent when in the workflow to call the tool. It does not explicitly state when not to use it or name alternatives, so it falls short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_delete_linkA
Delete a specific link from one note to another.
Unlike slipbox_remove_link, this tool returns an error if no link exists between the two notes.
Args: source_id: ID of the source note (the note containing the link) target_id: ID of the target note (the note being linked to)
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | Yes | ||
| target_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It clearly conveys deletion and adds a meaningful edge-case behavior: it errors when no link exists. It does not discuss side effects, permissions, or reversibility, but for a targeted link deletion this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, with the primary action first, the differentiating behavior second, and parameter explanations last. Every sentence earns its place with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter tool, the description covers the action, the key error condition, both parameter roles, and the distinction from the closest sibling. An output schema exists, so return-value details do not need to be spelled out here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by explaining both parameters: source_id is the note containing the link, and target_id is the note being linked to. This adds real meaning beyond the bare schema property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Delete a specific link from one note to another.' It also distinguishes itself from the sibling slipbox_remove_link by noting its error behavior, so an agent can tell the two apart without inspecting schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly names the sibling slipbox_remove_link and explains the key difference: this tool returns an error if no link exists. This makes the choice between the two tools clear, though it stops short of stating an explicit 'use this when you need...' rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_delete_noteA
Delete a note permanently.
Warning: This also removes all links to and from this note. Consider updating note_type to "fleeting" instead if uncertain.
Args: note_id: The ID of the note to delete
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It warns that the deletion is permanent and that 'This also removes all links to and from this note', which are the critical side effects an agent must know before invoking the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the purpose and the destructive warning appear immediately, and the Args section is minimal without unnecessary padding. Every sentence serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations, the description covers the essential facts: irreversible deletion, link side effects, and a safer alternative. An output schema exists, so return-value documentation is not required, and nothing necessary for correct invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the single parameter. However, it only restates 'The ID of the note to delete', which adds little beyond the parameter name 'note_id'. It omits any guidance on the ID format, where to obtain it, or whether any validation constraints apply.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Delete a note permanently' – a specific verb, resource, and degree of effect. It distinguishes this from slipbox_delete_link and slipbox_update_note, and the context makes the destructive scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly advises 'Consider updating note_type to "fleeting" instead if uncertain', naming a concrete alternative for the uncertain case. This gives clear when-to-use and when-not-to-use guidance, with no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_dismiss_clusterA
Permanently dismiss a cluster from maintenance suggestions.
Use this when a cluster has been reviewed and determined not to need a structure note, or when the user doesn't want to be reminded about it.
Args: cluster_id: The cluster ID to dismiss (e.g. "poetry-craft-revision")
| Name | Required | Description | Default |
|---|---|---|---|
| cluster_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing safety and effects. It clearly states the action is 'permanently' applied and scoped to maintenance suggestions, which indicates irreversibility. It could go further by clarifying that no notes are deleted, but the permanence and scope are sufficiently conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: it states the core purpose in the first sentence, gives a short usage rationale, and includes a minimal Args section with an example. No sentence is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one required parameter and an output schema present, so return-value details are unnecessary. The description covers purpose, usage timing, permanence, and parameter example. A small gap is that it does not tell the user how to discover cluster IDs, but that is likely covered by sibling tools like get_cluster_report.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for cluster_id, so the description must compensate. It adds a concrete example ('poetry-craft-revision') and identifies the parameter as a cluster ID, but beyond that it mostly restates the parameter name. For a single simple string parameter, this is adequate but not enriching.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('dismiss') with a clear resource ('cluster') and scope ('from maintenance suggestions'). It clearly distinguishes this from sibling tools like create_structure_from_cluster by positioning it as the 'don't remind me' action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: after a cluster has been reviewed and determined not to need a structure note, or when the user doesn't want reminders. It provides clear context but does not name or exclude specific alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_find_central_notesA
Find the most connected notes in the Zettelkasten.
Central notes have the most incoming and outgoing links, making them key hubs in your knowledge network. Good candidates for hub notes.
Args: limit: Maximum results (default: 10)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It conveys a non-mutating query ('Find') and explains the central-note concept via incoming/outgoing links, but it does not state how results are ordered, how ties are handled, or whether 'limit' truncates a fully computed ranking. These are moderate gaps for a no-annotation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and keeps explanatory text to two useful sentences before the Args block. It is compact and readable, though the Args section mostly duplicates the schema's default value rather than adding new information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-optional-parameter tool with an output schema and no annotations, the description covers the essential purpose, the definition of central notes, and the limit parameter. It does not detail sorting or algorithm internals, but those are unlikely to be required for correct invocation; the main missing piece is explicit sibling-tool routing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only one parameter with 0% description coverage, and the description adds 'Maximum results (default: 10)', which clarifies that limit means result count and matches the schema default. It provides no guidance on allowed ranges, zero/negative values, or truncation behavior, but the parameter is simple enough that this is only a minor gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Find the most connected notes in the Zettelkasten', a specific verb+resource statement, then defines central notes as hubs with the most incoming and outgoing links. This clearly distinguishes the tool from siblings like find_orphaned_notes or find_similar_notes by focusing on graph centrality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Good candidates for hub notes' implies the intended use case of identifying key knowledge hubs. However, it never explicitly tells an agent when to prefer this tool over the many sibling search/find tools, nor when not to use it, so the guidance is implied rather than fully actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_find_orphaned_notesA
Find notes with no connections to other notes.
Orphaned notes represent unintegrated knowledge. Review these periodically to either link them to existing notes or identify candidates for deletion.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavior. It correctly implies a read-only operation via the verb 'Find' and adds the interpretive context that orphaned notes represent unintegrated knowledge. However, it does not explicitly state side-effect freedom, output behavior, or other operational traits beyond the core result.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loads the primary function in the first sentence, and each subsequent sentence adds meaningful context about why orphaned notes matter and what to do with the results. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only discovery tool with an output schema available, the description covers what the tool returns, why it matters, and how to act on the results. Nothing essential for invoking it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema coverage is 100%, so parameter documentation is unnecessary. Baseline for 0 params is 4; the description adds no parameter details but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Find notes with no connections to other notes.' This clearly distinguishes the tool from siblings like slipbox_find_similar_notes and slipbox_find_central_notes, because it targets a unique criterion: absence of connections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: 'Review these periodically to either link them to existing notes or identify candidates for deletion.' It does not explicitly name alternative tools or state when not to use it, but the periodic-review purpose is useful and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_find_similar_notesA
Find notes similar to a given note.
Similarity is based on shared tags, common links, and content overlap. Useful for discovering connections you might have missed.
Args: note_id: ID of the reference note threshold: Minimum similarity score 0.0-1.0 (default: 0.3) limit: Maximum results (default: 5)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| note_id | Yes | ||
| threshold | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only operation through 'Find', but it does not explicitly state that it does not modify notes or that it is non-destructive. It does explain the similarity basis, which adds useful behavioral context, but it omits details about error behavior (e.g., missing note_id) or any side effects. The read-only nature is implied, not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured. The primary purpose is front-loaded in the first sentence, followed by a brief explanation of similarity logic and then a clear Args list. No redundant or filler content; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only query tool with an output schema, the description covers the core purpose, similarity logic, and parameters. It does not mention result ordering, behavior on nonexistent note_id, or any limitations, but these are minor given the output schema exists and the tool is straightforward. The description is nearly complete; a small gap is the lack of explicit note that the note must exist or that results are returned in any particular order.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It does so thoroughly in the Args section: note_id as 'ID of the reference note', threshold as 'Minimum similarity score 0.0-1.0 (default: 0.3)', and limit as 'Maximum results (default: 5)'. This adds meaning beyond the bare schema, including ranges and defaults, fully enabling correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Find notes similar to a given note' with a specific verb (Find) and resource (notes similar to a reference). It further distinguishes itself from sibling tools by specifying similarity criteria (shared tags, common links, content overlap), making it distinct from search, central, or orphaned note tools. No ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a usage hint: 'Useful for discovering connections you might have missed.' This implies when to use it, but it does not explicitly contrast it with alternatives like slipbox_search_notes or slipbox_get_linked_notes. There is no explicit statement of when not to use this tool or what conditions select it over siblings. The guidance is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_all_tagsA
Get all tags in the Zettelkasten.
Returns alphabetically sorted list of all tags. Use this to find existing tags before creating new notes to maintain tag consistency across your knowledge base.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden of behavioral disclosure. It transparently states the read-only nature through the verb 'Get', discloses the alphabetical sorting behavior, and implies no side effects, which is sufficient for this simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tight and front-loaded: purpose sentence, output behavior sentence, and usage guidance sentence. Every sentence contributes essential information with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only tool with an output schema, the description is fully sufficient. It explains what is returned, the ordering, and when to use it, leaving no operational ambiguity for an agent deciding to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters)Skip, so there is no parameter semantics to explain. The schema coverage is effectively complete for an empty properties object, and the description adds value by describing the result rather than needing to describe inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb-resource pair ('Get all tags in the Zettelkasten') and defines the output as an alphabetically sorted list. This clearly distinguishes it from sibling tools that operate on notes or links rather than tags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using this tool before creating new notes to ensure tag consistency, giving a concrete when-to-use scenario. It does not explicitly name alternatives or state when not to use it, but the context is clear for a simple zero-parameter getter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_cluster_reportA
Get pending cluster analysis for structure note creation.
Clusters are groups of notes sharing tags but lacking a structure note. High-scoring clusters are good candidates for new structure notes.
Uses cached analysis by default. Set refresh=true to regenerate. Cluster analysis runs automatically via cron if configured.
Scoring factors:
Note count (7-15 is ideal, >15 is overdue)
Orphan ratio (more orphans = more urgent)
Internal link density (fewer links = needs structure)
Recency (recent activity = active domain)
Args: min_score: Minimum cluster score 0.0-1.0 (default: 0.5) limit: Maximum clusters to return (default: 5) include_notes: Include full note list per cluster (default: false) refresh: Force regeneration of cluster analysis (default: false)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| refresh | No | ||
| min_score | No | ||
| include_notes | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses caching (default) and refresh behavior, automatic cron execution, and the scoring factors that influence results. It does not explicitly state that the operation is read-only (aside from the word 'get'), nor does it mention any auth or rate-limit requirements. Still, the disclosure of caching, refresh side effects, and scoring logic goes well beyond the schema, so a 4 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a one-sentence purpose, a brief explanation, a bulleted list of scoring factors, and a clear Args block. It is not bloated, but the scoring factors could arguably be moved to a separate section without harming clarity. It is front-loaded with the core purpose, so it earns a 4.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (which covers return values) and the fact that all four parameters are fully explained in the description, the tool is complete. The description covers purpose, behavior, scoring, and parameter semantics. No critical missing pieces like error handling or timeouts are necessary because the output schema and the tool's simple nature make them non-essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It does: the 'Args' section explains each parameter with meaning and range (min_score 0.0-1.0), behavior (limit max clusters), and implications (include_notes, refresh). This adds significant value beyond the bare schema types and defaults, making it clear how each parameter affects the result.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear, specific statement: 'Get pending cluster analysis for structure note creation.' It then explains what clusters are and how they relate to structure notes, distinguishing this tool from siblings like slipbox_refresh_clusters and slipbox_create_structure_from_cluster. The verb 'get' and the resource 'cluster analysis' are unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides strong contextual guidance: it explains that high-scoring clusters are candidates for structure notes, and mentions the caching behavior and automatic cron runs. However, it does not explicitly compare this tool to siblings like slipbox_refresh_clusters or slipbox_dismiss_cluster, nor does it state when to use an alternative. The guidance is implied rather than explicit, so it earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_linked_notesA
Get notes linked to or from a specific note.
Use this to explore the knowledge graph around a note.
Directions:
outgoing: Notes this note links TO
incoming: Notes that link TO this note
both: All connected notes in either direction
Args: note_id: ID of the note to explore from direction: One of outgoing/incoming/both (default: both)
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes | ||
| direction | No | both |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral disclosure burden. It explains what the tool returns (linked notes), how direction affects the result set, and the default 'both' behavior. The imperative 'Get' implies a read-only operation, and no conflicting destructive behavior is suggested.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by a usage hint, a compact direction list, and an Args block. Every sentence adds useful information; there is no filler or over-elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema available and only two parameters, the description covers the behavioral purpose, parameter semantics, directions, and default value. The only minor gap is the absence of explicit guidance on when to prefer sibling graph-analysis tools, but this does not block correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so parameter documentation falls entirely on the description. It explains note_id as the starting point and direction with its allowed values and default. It could add more constraints for note_id, but the provided semantics are sufficient for a two-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a precise verb-resource pair: 'Get notes linked to or from a specific note.' It defines outgoing/incoming/both directions and frames the tool as knowledge-graph exploration, clearly distinguishing it from single-note retrieval or search tools like slipbox_get_note or slipbox_search_notes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Use this to explore the knowledge graph around a note' gives a clear context for when to call it, and the direction definitions clarify the kind of traversal intended. It does not explicitly say when not to use it or name a sibling alternative, but the intended use is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_noteA
Retrieve a note by ID or title.
Returns full note content including metadata, tags, and links. Use this to read note contents before creating links or updates.
Args: identifier: Either the note ID (e.g. "20251217T172432480464000") or exact title
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It effectively communicates read-only behavior and details what the response contains ('full note content including metadata, tags, and links'), which goes beyond the schema. It does not cover failure modes such as missing identifiers, but this is a minor gap for a simple retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded, and every line earns its place: purpose, return contents, usage guidance, and parameter meaning. No filler or irrelevant detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read tool with an output schema present, the description covers the essential aspects: what the tool does, how to identify the note, and when to use it. It lacks explicit not-found or error behavior, but that is not critical for normal invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the bare 'identifier' parameter. It does so by explaining that the identifier can be a note ID or exact title and gives a concrete ID format example. Ambiguity handling between the two forms is not addressed, but the compensation is strong for a single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action ('Retrieve a note') with resource and lookup method ('by ID or title'), and clarifies the result includes full content, metadata, tags, and links. This clearly differentiates it from sibling read tools like search_notes or get_linked_notes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use this tool 'to read note contents before creating links or updates,' which is a clear usage context tied to relevant siblings. It does not mention when NOT to use it or name alternatives, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_list_notes_by_dateA
List notes by creation or update date.
Useful for reviewing recent work or finding notes from a specific period.
Args: start_date: Start date in ISO format YYYY-MM-DD (optional) end_date: End date in ISO format YYYY-MM-DD (optional) use_updated: If true, filter by updated_at instead of created_at (default: false) limit: Maximum results (default: 10)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| end_date | No | ||
| start_date | No | ||
| use_updated | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains filtering by created_at or updated_at, optional date bounds, and the default limit. However, it does not disclose result ordering, how date boundaries are handled, or pagination behavior, leaving some uncertainty about exact behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded with the primary action, and organized with a clear Args block. It is slightly redundant with the schema's property definitions, but since the schema lacks descriptions, the explicit parameter documentation is justified and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only listing tool with an output schema, the description covers the essential invocation details and parameter semantics. It does not discuss ordering or edge cases, but the output schema and the simplicity of the operation make the definition sufficiently complete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It explains every parameter beyond the raw schema: ISO date format for start_date/end_date, the true/false semantics of use_updated, and the default limit. This is exactly the kind of parameter-level guidance an agent needs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'List notes by creation or update date.' This makes the tool's purpose immediately clear and distinguishes it from content-based or similarity-based sibling tools. It also gives practical use cases, reinforcing what the tool is for.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states when the tool is appropriate: 'Useful for reviewing recent work or finding notes from a specific period.' It provides clear context, though it does not explicitly point to alternatives such as slipbox_search_notes for content-based lookup or when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_rebuild_indexA
Rebuild the database index from markdown files.
Use this if notes were edited outside the MCP server or if the database seems out of sync with the filesystem.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states what the tool does (rebuild index) but does not reveal any side effects, such as whether it overwrites the existing index, if it destroys data, if it requires exclusive access, or if it is safe to run concurrently. For a maintenance operation that modifies state, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero wasted words. The action is front-loaded, and the usage context is delivered in the second sentence. It is appropriately concise for a zero-parameter maintenance tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and an output schema present, the description provides the essential trigger conditions. However, it omits behavioral details like whether the operation is destructive, if it locks the database, or if it can be run while other operations are in progress. Given no annotations, the description should disclose more about the operation's impact, so a 3 is appropriate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so according to the scoring rule the baseline is 4. The description adds no parameter-specific information, but none is needed; the schema already confirms an empty parameter set. The description's clarity about the action suffices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('rebuild'), resource ('database index'), and source ('from markdown files'), which clearly distinguishes it from sibling tools that handle notes, links, clusters, or searches. There is no other rebuild-oriented sibling, so the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: when notes were edited outside the MCP server or the database seems out of sync with the filesystem. It provides clear context for invocation but does not mention alternatives or exclusions, which is acceptable since this is a unique maintenance operation with no direct sibling alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_refresh_clustersA
Regenerate cluster analysis and save report.
Analyzes all notes for emergent clusters based on:
Tag co-occurrence (tags that frequently appear together)
Connection patterns (notes that link to each other)
Structure note coverage (which clusters already have structure notes)
Results saved to cluster-analysis.json next to the vault's SQLite index (or SLIPBOX_CLUSTER_REPORT_PATH when set)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It clearly states the tool scans all notes, uses three clustering signals, and writes results to cluster-analysis.json or SLIPBOX_CLUSTER_REPORT_PATH. It does not explicitly say whether the existing report is overwritten or whether the SQLite index must exist, but the 'regenerate' and 'saved to' language covers the primary side effect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is a crisp summary, followed by a scannable bulleted list of analysis criteria and a clear statement of the output location. Every sentence adds necessary information with no repetition or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter command with an output schema available, the description provides the essential execution facts: what is analyzed, how clusters are formed, and where the report is written. It would be slightly stronger with an explicit pointer to slipbox_get_cluster_report for reading the saved report, but that gap does not block correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the empty schema is fully self-documenting, so the baseline of 4 applies. The description enhances understanding by explaining what the analysis is based on, even though there are no parameter meanings to clarify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening sentence names a specific operation ('Regenerate cluster analysis and save report'), and the bulleted list defines the analysis scope. This distinguishes it from siblings like slipbox_get_cluster_report and slipbox_dismiss_cluster by focusing on regeneration and report writing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this should be used when refreshed cluster analysis is needed, and it states the analysis dimensions. However, it does not explicitly contrast it with slipbox_get_cluster_report for reading existing reports or mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_remove_linkB
Remove a link between two notes.
Args: source_id: ID of the source note target_id: ID of the target note bidirectional: If true, removes links in both directions
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | Yes | ||
| target_id | Yes | ||
| bidirectional | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It states the removal operation and the bidirectional option, but does not disclose idempotency, behavior when the link does not exist, side effects, or whether the operation is reversible—important gaps for a destructive action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: one clear purpose sentence followed by a scannable argument list. There is no filler or repetition beyond the necessary parameter explanations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple three-parameter scalar tool with an output schema, the core invocation is understandable. However, the lack of sibling differentiation and the absence of behavioral guidance around a destructive operation make the description incomplete for a fully informed tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds definitions for source_id, target_id, and bidirectional, which is helpful. However, the definitions are minimal and mostly restate the parameter names; they do not specify ID formats, lookup semantics, or additional constraints beyond what the schema already shows.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action and resource: 'Remove a link between two notes.' However, it does not differentiate this tool from the sibling slipbox_delete_link, which appears to have an overlapping purpose, so it stops short of full sibling-level clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool instead of slipbox_delete_link or any other alternative. It only explains parameters, leaving the agent to infer the intended context from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_search_notesA
Search for notes by text, tags, or type.
Searches across titles and content. Combine parameters for precise filtering.
Examples:
Search by topic: query="poetry revision"
Filter by tag: tags="craft,poetry"
Find structure notes: note_type="structure"
Combined: query="metaphor" tags="poetry" limit=5
Args: query: Text to search in titles and content (optional) tags: Comma-separated tags to filter by, e.g. "poetry,craft" (optional) note_type: Filter by type: fleeting/literature/permanent/structure/hub (optional) limit: Maximum results to return (default: 10)
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| limit | No | ||
| query | No | ||
| note_type | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention any side effects, permissions, ordering, pagination, or behavior when no query is supplied (e.g., returns all notes). It only states the action, not the consequences or edge cases. This is a significant gap for a search tool that could return unexpected results if called with empty parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence, followed by explanatory text and a formatted Args section. It uses examples to illustrate usage without being verbose. It could be slightly more concise in the intro, but the structure is logical and front-loaded with the core purpose. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 optional parameters and no required ones, the description covers each parameter's semantics well. However, it omits edge-case behavior such as what happens with no parameters (does it return all notes?) or how results are ordered. Since an output schema exists, return format is covered, but behavioral edge cases are not. It's adequate for basic usage but leaves some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It does this thoroughly: query searches titles and content, tags are comma-separated, note_type lists allowed values (fleeting/literature/permanent/structure/hub), and limit has a default of 10. Examples illustrate parameter combinations. This adds substantial meaning beyond the schema's minimal type/default information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search for notes by text, tags, or type' and elaborates that it searches across titles and content. This is specific and distinct from sibling tools like slipbox_get_note (retrieves a single note) or slipbox_list_notes_by_date (lists by date). The verb 'search' and resource 'notes' are explicit, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete examples for each parameter combination, demonstrating when to use query, tags, note_type, and limit. It implies usage scenarios clearly. While it doesn't explicitly mention alternatives or when not to use this tool, the sibling tools have distinct purposes, so an agent can infer appropriate usage. It's strong but lacks explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_update_noteA
Update an existing note.
Only provided fields are updated; omitted fields remain unchanged. Pass empty string for tags to clear all tags. Pass empty string for references to clear all references.
Constraint: notes with note_type='literature' must have at least one reference. If you are promoting a note to 'literature', pass the citation in references in the same call.
Args: note_id: The ID of the note to update title: New title (optional) content: New content (optional) note_type: New type: fleeting/literature/permanent/structure/hub (optional) tags: New comma-separated tags, or empty string to clear (optional) references: New newline-separated citations, or empty string to clear (optional)
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| title | No | ||
| content | No | ||
| note_id | Yes | ||
| note_type | No | ||
| references | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses that omitted fields remain unchanged, that empty strings clear tags/references, and that literature notes require at least one reference. It does not cover error behavior for missing note_ids or response details, but the key behavioral traits are surfaced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well organized: behavioral notes come first, followed by a compact Args list. There is minor redundancy between the prose and the Args section regarding empty-string clearing, but no wasted sentences overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no schema descriptions, the description covers all six parameters plus the critical literature-reference constraint. An output schema exists, so return-value documentation is not required. It could add behavior for nonexistent notes, but the description is substantially complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the Args section fully compensates by explaining every parameter's meaning, allowed note_type values, comma-separated tags, newline-separated references, and empty-string clearing behavior. This is exactly the kind of parameter-level detail an agent needs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Update' and the resource 'existing note', so it is unambiguous that this tool mutates an existing slipbox note. It does not explicitly differentiate from siblings like slipbox_create_note or slipbox_delete_note, but the verb+resource combination leaves little room for confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys when to use the tool by explaining partial-update semantics and the promotion constraint to literature notes. It does not explicitly say 'use this instead of create/delete' or provide when-not-to-use guidance, but the context is clear enough that an agent can route appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
19 tool updates
v1.5.4- Changed
slipbox_create_link1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_create_linkOutput", + "type": "object" +}
- Changed
slipbox_create_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_create_noteOutput", + "type": "object" +}
- Changed
slipbox_create_structure_from_cluster1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_create_structure_from_clusterOutput", + "type": "object" +}
- Changed
slipbox_delete_link1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_delete_linkOutput", + "type": "object" +}
- Changed
slipbox_delete_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_delete_noteOutput", + "type": "object" +}
- Changed
slipbox_dismiss_cluster1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_dismiss_clusterOutput", + "type": "object" +}
- Changed
slipbox_find_central_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_find_central_notesOutput", + "type": "object" +}
- Changed
slipbox_find_orphaned_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_find_orphaned_notesOutput", + "type": "object" +}
- Changed
slipbox_find_similar_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_find_similar_notesOutput", + "type": "object" +}
- Changed
slipbox_get_all_tags1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_all_tagsOutput", + "type": "object" +}
- Changed
slipbox_get_cluster_report1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_cluster_reportOutput", + "type": "object" +}
- Changed
slipbox_get_linked_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_linked_notesOutput", + "type": "object" +}
- Changed
slipbox_get_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_noteOutput", + "type": "object" +}
- Changed
slipbox_list_notes_by_date1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_list_notes_by_dateOutput", + "type": "object" +}
- Changed
slipbox_rebuild_index1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_rebuild_indexOutput", + "type": "object" +}
- Changed
slipbox_refresh_clusters1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_refresh_clustersOutput", + "type": "object" +}
- Changed
slipbox_remove_link1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_remove_linkOutput", + "type": "object" +}
- Changed
slipbox_search_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_search_notesOutput", + "type": "object" +}
- Changed
slipbox_update_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_update_noteOutput", + "type": "object" +}
19 tool updates
v0.1.0- First observed
slipbox_create_link - First observed
slipbox_create_note - First observed
slipbox_create_structure_from_cluster - First observed
slipbox_delete_link - First observed
slipbox_delete_note - First observed
slipbox_dismiss_cluster - First observed
slipbox_find_central_notes - First observed
slipbox_find_orphaned_notes - First observed
slipbox_find_similar_notes - First observed
slipbox_get_all_tags - First observed
slipbox_get_cluster_report - First observed
slipbox_get_linked_notes - First observed
slipbox_get_note - First observed
slipbox_list_notes_by_date - First observed
slipbox_rebuild_index - First observed
slipbox_refresh_clusters - First observed
slipbox_remove_link - First observed
slipbox_search_notes - First observed
slipbox_update_note
TDQS
Scored across 19 tools
Most tools target a distinct resource/action, but slipbox_delete_link and slipbox_remove_link are near-duplicates that differ only in error behavior, and get_cluster_report with refresh=true overlaps with refresh_clusters. The descriptions clarify the differences, but an agent could still easily select the wrong tool.
Names consistently follow a slipbox_verb_noun pattern with clear domain vocabulary. Minor inconsistencies remain: delete_link vs remove_link use different verbs for the same logical operation, and search_notes sits alongside find_similar_notes, find_central_notes, and find_orphaned_notes.
At 19 tools, this is above the typical well-scoped range, but the extra surface is justified by distinct note CRUD, link management, graph analytics, and cluster maintenance features. It feels slightly heavy rather than bloated.
The server covers note and link lifecycles, search and tag discovery, graph analysis, and cluster-driven structure creation, so core Zettelkasten workflows are supported. Minor gaps exist, such as no dedicated list-all-notes tool and link edits requiring delete/recreate, but agents can work around them.
Maintenance
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