The Zettelkasten MCP server enables AI-assisted knowledge management using the Zettelkasten methodology, allowing you to create, organize, link, and explore atomic notes through Claude and other MCP clients.
Note Management: Create, retrieve, update, and delete notes with different types (fleeting, literature, permanent, structure, hub), titles, content, and tags.
Semantic Linking: Build rich, bidirectional connections between notes using relationship types like extends, refines, contradicts, questions, supports, references, and related.
Search & Discovery: Search notes by content, tags, or type; find similar notes using similarity metrics; discover central hub notes with most connections; identify orphaned notes with no links; list notes by creation or update date within specified ranges; view all available tags.
Knowledge Graph Exploration: Traverse relationships by getting linked notes in any direction (incoming, outgoing, or both) to analyze your knowledge network structure.
Dual Storage System: Maintains human-readable Markdown files with YAML frontmatter as source of truth while using a database index for efficient querying, enabling both direct file editing and AI-assisted management.
Database Flexibility: Supports multiple database backends including SQLite (default), PostgreSQL, MySQL, and MS SQL Server via SQLAlchemy URLs.
System Maintenance: Rebuild the database index from Markdown files when editing notes externally to maintain data integrity.
Supports placing Markdown notes under Git version control for tracking changes to the knowledge base
Stores all notes as human-readable Markdown files with YAML frontmatter for metadata, enabling direct editing in any text editor and version control
Uses SQLite database as an indexing layer for efficient querying, searching, and traversing the knowledge graph while maintaining relationship information
Uses YAML frontmatter in Markdown files to store and manage note metadata
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., "@zettelkasten-mcp create a fleeting note about the benefits of atomic thinking"
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.
A Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology, allowing you to create, link, explore and synthesize atomic notes through Claude and other MCP-compatible clients.
The Zettelkasten method is a knowledge management system developed by German sociologist Niklas Luhmann, who used it to produce over 70 books and hundreds of articles. It consists of three core principles:
Atomicity: Each note contains exactly one idea, making it a discrete unit of knowledge
Connectivity: Notes are linked together to create a network of knowledge, with meaningful relationships between ideas
Emergence: As the network grows, new patterns and insights emerge that weren't obvious when the individual notes were created
What makes the Zettelkasten approach powerful is how it enables exploration in multiple ways:
Vertical exploration: dive deeper into specific topics by following connections within a subject area.
Horizontal exploration: discover unexpected relationships between different fields by traversing links that cross domains.
This structure invites serendipitous discoveries as you follow trails of thought from note to note, all while keeping each piece of information easily accessible through its unique identifier. Luhmann called his system his "second brain" or "communication partner" - this digital implementation aims to provide similar benefits through modern technology.
Related MCP server: Zettelkasten MCP Server
Create atomic notes with unique timestamp-based IDs
Link notes bidirectionally to build a knowledge graph
Tag notes for categorical organization
Search notes by content, tags, or links
Use markdown format for human readability and editing
Integrate with Claude through MCP for AI-assisted knowledge management
Dual storage architecture (see below)
Synchronous operation model for simplified architecture
Knowledge creation: A small Zettelkasten knowledge network about the Zettelkasten method itself
The Zettelkasten MCP server supports different types of notes:
Type | Handle | Description |
Fleeting notes |
| Quick, temporary notes for capturing ideas |
Literature notes |
| Notes from reading material |
Permanent notes |
| Well-formulated, evergreen notes |
Structure notes |
| Index or outline notes that organize other notes |
Hub notes |
| Entry points to the Zettelkasten on key topics |
The Zettelkasten MCP server uses a comprehensive semantic linking system that creates meaningful connections between notes. Each link type represents a specific relationship, allowing for a rich, multi-dimensional knowledge graph.
Primary Link Type | Inverse Link Type | Relationship Description |
|
| Simple reference to related information (symmetric relationship) |
|
| One note builds upon or develops concepts from another |
|
| One note clarifies or improves upon another |
|
| One note presents opposing views to another |
|
| One note poses questions about another |
|
| One note provides evidence for another |
|
| Generic relationship (symmetric relationship) |
To ensure maximum effectiveness, we recommend using a system prompt ("project instructions"), project knowledge, and an appropriate chat prompt when asking the LLM to process information, or explore or synthesize your Zettelkasten notes. The docs directory in this repository contains the necessary files to get you started:
Pick one:
For end users:
(more info relevant to your project)
For developers and contributors:
NB: Optionally include the source code with a tool like repomix.
This system uses a dual storage approach:
Markdown Files: All notes are stored as human-readable Markdown files with YAML frontmatter for metadata. These files are the source of truth and can be:
Edited directly in any text editor
Placed under version control (Git, etc.)
Backed up using standard file backup procedures
Shared or transferred like any other text files
Database Index: Functions as an indexing layer that:
Facilitates efficient querying and search operations
Enables Claude to quickly traverse the knowledge graph
Maintains relationship information for faster link traversal
Is automatically rebuilt from Markdown files when needed
Uses SQLite by default; provide a SQLAlchemy URL via ZETTELKASTEN_DATABASE for PostgreSQL or other backends
If you edit Markdown files directly outside the system, you'll need to run the zk_rebuild_index tool to update the database. The database itself can be deleted at any time - it will be regenerated from your Markdown files.
To install Zettelkasten MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install zettelkasten-mcp --client claudeuvx --from=git+https://github.com/entanglr/zettelkasten-mcp zettelkasten-mcp --notes-dir ./data/notes --database ./data/db/zettelkasten.dbInstall from GitHub:
pipx install "git+https://github.com/entanglr/zettelkasten-mcp.git"If/when published on PyPI, install from PyPI instead:
pipx install zettelkasten-mcpRun the server:
zettelkasten-mcp --notes-dir ./data/notes --database ./data/db/zettelkasten.dbOn Apple Silicon Macs, prefer pipx/uvx for native darwin/arm64
execution. Docker images are Linux-only (linux/amd64, linux/arm64) and do
not provide a native darwin container target.
The latest image tag is published as a multi-arch image manifest with:
linux/amd64
linux/arm64
Backend support in the container image differs by architecture:
Backend |
|
|
SQLite | Supported | Supported |
PostgreSQL | Supported | Supported |
MySQL/MariaDB | Supported | Supported |
SQL Server | Supported | Not supported |
linux/arm64 images intentionally skip SQL Server ODBC driver installation
(msodbcsql18) because upstream driver packaging is currently amd64-focused in
this Docker build strategy.
# Clone the repository
git clone https://github.com/entanglr/zettelkasten-mcp.git
cd zettelkasten-mcp
# Create a virtual environment with uv
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
uv add "mcp[cli]"
# Install dev dependencies
uv sync --all-extrasCreate a .env file in the project root by copying the example:
cp .env.example .envThen edit the file to configure your connection parameters.
To use PostgreSQL instead of the bundled SQLite database, install the optional driver and point ZETTELKASTEN_DATABASE at a SQLAlchemy URL:
pip install "zettelkasten-mcp[postgresql]"
export ZETTELKASTEN_DATABASE="postgresql+psycopg://user:password@localhost:5432/zettelkasten"Using pipx:
pipx install "zettelkasten-mcp[postgresql]"
export ZETTELKASTEN_DATABASE="postgresql+psycopg://user:password@localhost:5432/zettelkasten"ZETTELKASTEN_DATABASE accepts either a filesystem path (default ./data/db/zettelkasten.db) or any SQLAlchemy-compatible URL. Legacy ZETTELKASTEN_DATABASE_PATH / ZETTELKASTEN_DATABASE_URL variables are still honored for backward compatibility.
For MySQL or MariaDB support, install the mysql extra and supply a URL using the pymysql driver:
pip install "zettelkasten-mcp[mysql]"
export ZETTELKASTEN_DATABASE="mysql+pymysql://user:password@localhost:3306/zettelkasten"Using pipx:
pipx install "zettelkasten-mcp[mysql]"
export ZETTELKASTEN_DATABASE="mysql+pymysql://user:password@localhost:3306/zettelkasten"For Microsoft SQL Server, install the sqlserver extra and point to an ODBC connection string (requires the appropriate system ODBC driver, e.g. ODBC Driver 18 for SQL Server):
pip install "zettelkasten-mcp[sqlserver]"
export ZETTELKASTEN_DATABASE="mssql+pyodbc://user:password@server/database?driver=ODBC+Driver+18+for+SQL+Server"Using pipx:
pipx install "zettelkasten-mcp[sqlserver]"
export ZETTELKASTEN_DATABASE="mssql+pyodbc://user:password@server/database?driver=ODBC+Driver+18+for+SQL+Server"Note for container users: SQL Server support is available in the published
Docker image on linux/amd64 only. On linux/arm64, the container does not
include SQL Server ODBC drivers, so use SQLite/PostgreSQL/MySQL instead.
python -m zettelkasten_mcpOr with explicit configuration:
python -m zettelkasten_mcp --notes-dir ./data/notes --database ./data/db/zettelkasten.dbOr with PostgreSQL:
python -m zettelkasten_mcp --notes-dir ./data/notes \
--database postgresql+psycopg://user:password@localhost:5432/zettelkastennpx -y @smithery/cli install zettelkasten-mcp --client claudeAdd the following configuration to your Claude Desktop:
{
"mcpServers": {
"zettelkasten": {
"command": "/absolute/path/to/zettelkasten-mcp/.venv/bin/python",
"args": ["-m", "zettelkasten_mcp"],
"env": {
"ZETTELKASTEN_NOTES_DIR": "/absolute/path/to/zettelkasten-mcp/data/notes",
"ZETTELKASTEN_DATABASE": "postgresql+psycopg://user:password@localhost:5432/zettelkasten",
"ZETTELKASTEN_LOG_LEVEL": "INFO"
}
}
}
}Set ZETTELKASTEN_DATABASE to a filesystem path for SQLite or any SQLAlchemy URL (for example PostgreSQL) to choose the backend.
All tools have been prefixed with zk_ for better organization:
Tool | Description |
| Create a new note with a title, content, and optional tags |
| Retrieve a specific note by ID or title |
| Update an existing note's content or metadata |
| Delete a note |
| Create links between notes |
| Remove links between notes |
| Search for notes by content, tags, or links |
| Find notes linked to a specific note |
| List all tags in the system |
| Find notes similar to a given note |
| Find notes with the most connections |
| Find notes with no connections |
| List notes by creation/update date |
| Rebuild the database index from Markdown files |
zettelkasten-mcp/
├── src/
│ └── zettelkasten_mcp/
│ ├── models/ # Data models
│ ├── storage/ # Storage layer
│ ├── services/ # Business logic
│ └── server/ # MCP server implementation
├── data/
│ ├── notes/ # Note storage (Markdown files)
│ └── db/ # Database for indexing
├── tests/ # Test suite
├── .env.example # Environment variable template
└── README.mdComprehensive test suite for Zettelkasten MCP covering all layers of the application from models to the MCP server implementation.
From the project root directory, run:
python -m pytest -v tests/uv run pytest -v tests/uv run pytest --cov=zettelkasten_mcp --cov-report=term-missing tests/uv run pytest -v tests/test_models.pyuv run pytest -v tests/test_models.py::TestNoteModeluv run pytest -v tests/test_models.py::TestNoteModel::test_note_validationtests/
├── conftest.py - Common fixtures for all tests
├── test_integration.py - Integration tests for the entire system
├── test_mcp_server.py - Tests for MCP server tools
├── test_models.py - Tests for data models
├── test_note_repository.py - Tests for note repository
├── test_search_service.py - Tests for search service
├── test_semantic_links.py - Tests for semantic linking
└── test_zettel_service.py - Tests for zettel service⚠️ USE AT YOUR OWN RISK: This software is experimental and provided as-is without warranty of any kind. While efforts have been made to ensure data integrity, it may contain bugs that could potentially lead to data loss or corruption. Always back up your notes regularly and use caution when testing with important information.
This MCP server was crafted with the assistance of Claude, who helped organize the atomic thoughts of this project into a coherent knowledge graph. Much like a good Zettelkasten system, Claude connected the dots between ideas that might otherwise have remained isolated. Unlike Luhmann's paper-based system, however, Claude didn't require 90,000 index cards to be effective.
MIT License
To keep code style consistent, this project uses pre-commit with black and isort configured.
Install and enable the hooks locally:
pip install pre-commit
pre-commit install
pre-commit run --all-filesIf you use a virtual environment, make sure the environment is activated before running pre-commit install so the hooks point to the correct Python interpreter.
This project includes recommended settings for Visual Studio Code to enable automatic import completions and organize imports on save. The workspace settings are in .vscode/settings.json and recommended extensions in .vscode/extensions.json.
Recommended VS Code extensions:
ms-python.python — Python language support
ms-python.vscode-pylance — Pylance language server (provides auto-import completions)
Important settings (already configured):
python.analysis.autoImportCompletions: true — shows auto-import suggestions in completions
editor.codeActionsOnSave.source.organizeImports: true — runs import sorting on save
editor.formatOnSave: true with Black as the formatter
Activate the workspace settings by opening the project in VS Code; you may need to install the extensions and reload the window.
Create a link between two notes. Args: source_id: ID of the source note target_id: ID of the target note link_type: Type of link (reference, extends, refines, contradicts, questions, supports, related) description: Optional description of the link bidirectional: Whether to create a link in both directions
| Name | Required | Description | Default |
|---|---|---|---|
| bidirectional | No | ||
| description | No | ||
| link_type | No | reference | |
| source_id | Yes | ||
| target_id | Yes |
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 states the action 'create a link' but doesn't describe what happens on success/failure, whether links are mutable, if there are rate limits, or permission requirements. The description covers basic functionality but misses critical behavioral details for a mutation tool with no annotation support.
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 and appropriately sized, with a clear purpose statement followed by a bullet-point-like parameter list. Every sentence earns its place by providing essential information. It could be slightly more front-loaded with key behavioral details, but overall it's efficient and readable.
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 complexity (mutation tool with 5 parameters), no annotations, and no output schema, the description is moderately complete. It covers parameters well but lacks behavioral context (e.g., error handling, return values) and usage guidelines. For a tool that creates relationships between notes, more context on implications and alternatives would improve completeness.
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, which it does effectively by listing all 5 parameters with clear explanations. It adds meaning beyond the schema by specifying link_type options (e.g., 'reference', 'extends'), clarifying description as optional, and explaining bidirectional behavior. This significantly enhances parameter understanding despite the schema's lack of 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 clearly states the verb 'create' and the resource 'link between two notes', making the purpose immediately understandable. It distinguishes this from sibling tools like zk_remove_link (deletion) and zk_get_linked_notes (retrieval), though it doesn't explicitly contrast with all siblings. The purpose is specific but could be slightly more distinctive.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., existing note IDs), compare with similar tools like zk_update_note for modifying links, or indicate scenarios where creating links is appropriate versus other operations. Usage is implied by the action but lacks explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new Zettelkasten note. Args: title: The title of the note content: The main content of the note note_type: Type of note (fleeting, literature, permanent, structure, hub) tags: Comma-separated list of tags (optional)
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | ||
| note_type | No | permanent | |
| tags | No | ||
| title | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this creates a new note but doesn't describe what happens after creation (e.g., whether it returns the created note, assigns an ID, or how errors are handled). It mentions note_type options but doesn't explain their significance or default behavior. For a creation tool with zero annotation coverage, this leaves important behavioral aspects unspecified.
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 appropriately concise with a clear opening statement followed by parameter explanations. The parameter list is well-structured with brief but informative explanations. There's no unnecessary verbiage, though the formatting with indentation could be cleaner. Every sentence adds value without 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?
Given this is a creation tool with 4 parameters, 0% schema description coverage, no annotations, and no output schema, the description does an adequate but incomplete job. It covers the basic action and parameters but lacks information about what the tool returns, error conditions, or how it integrates with the Zettelkasten system. For a tool that presumably creates persistent data, more context about the creation outcome would be helpful.
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 description provides meaningful semantic information for all parameters beyond the schema's 0% coverage. It explains that 'title' is the note's title, 'content' is the main content, 'note_type' has specific allowed values with examples, and 'tags' are optional and comma-separated. This compensates well for the schema's lack of descriptions, though it doesn't elaborate on format requirements or constraints.
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 'Create' and the resource 'Zettelkasten note', making the purpose immediately understandable. It distinguishes this from sibling tools like zk_update_note or zk_delete_note by specifying it's for creating new notes. However, it doesn't explicitly differentiate from other creation tools like zk_create_link, which slightly reduces specificity.
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 no guidance on when to use this tool versus alternatives. It doesn't mention when to choose zk_create_note over zk_update_note for modifications, or how it relates to sibling tools like zk_create_link. There's no context about prerequisites, constraints, or typical use cases beyond the basic action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a note. Args: note_id: The ID of the note to delete
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It states the action ('Delete') but doesn't disclose critical traits: whether deletion is permanent/reversible, permission requirements, side effects (e.g., breaking links), error conditions, or response format. This is inadequate for a destructive operation.
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 brief and front-loaded with the core action, but the Args section is redundant with the schema and adds minimal value. The two-sentence structure is efficient, but the second sentence could be integrated more seamlessly or omitted since it repeats schema 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 destructive tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral consequences (e.g., permanence, error handling), usage context (e.g., prerequisites), and output expectations. Given the complexity of deletion operations, this leaves significant gaps for an AI agent.
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 description adds essential meaning beyond the schema, which has 0% coverage. It explains that 'note_id' is 'The ID of the note to delete', clarifying the parameter's purpose and relationship to the operation. This compensates well for the schema's lack of descriptions, though it doesn't detail ID format or sourcing.
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 ('Delete') and resource ('a note'), making the purpose immediately understandable. It distinguishes from siblings like 'zk_update_note' (update) and 'zk_get_note' (retrieve), though it doesn't explicitly contrast with deletion alternatives like 'zk_remove_link' (which removes relationships rather than 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing note ID), exclusions (e.g., not for bulk deletion), or related tools like 'zk_remove_link' for link removal instead of note deletion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find notes with the most connections (incoming + outgoing links). Notes are ranked by their total number of connections, determining their centrality in the knowledge network. Due to database constraints, only one link of each type is counted between any pair of notes.
Args:
limit: Maximum number of results to return (default: 10)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a read operation (implied by 'Find'), discloses a constraint ('only one link of each type is counted between any pair of notes'), and explains the ranking logic. However, it does not cover aspects like performance, error handling, or output format, leaving some gaps in transparency.
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 appropriately sized and front-loaded, starting with the core purpose. The explanation of ranking and constraints is necessary, and the parameter documentation is concise. However, the formatting with indentation and blank lines slightly reduces efficiency, but overall, most sentences earn their place without waste.
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's moderate complexity (ranking based on connections), no annotations, and no output schema, the description is partially complete. It covers the purpose, constraint, and parameter semantics well, but lacks details on the output format, error conditions, or performance considerations, which would enhance completeness for an agent's use.
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 input schema has 0% description coverage, so the description must compensate. It adds meaningful semantics for the single parameter 'limit' by explaining its purpose ('Maximum number of results to return') and default value. This fully compensates for the schema gap, though it does not provide additional details like range constraints or examples.
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 specific action ('Find notes with the most connections'), identifies the resource ('notes'), and distinguishes from siblings by focusing on centrality ranking rather than other criteria like similarity, orphan status, or date. It explains the ranking methodology ('ranked by their total number of connections') and the scope ('incoming + outgoing links'), making the purpose explicit and differentiated.
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 usage for identifying central notes in a knowledge network, but does not explicitly state when to use this tool versus alternatives like 'zk_find_similar_notes' or 'zk_get_linked_notes'. It mentions 'database constraints' which provides some context, but lacks clear guidance on scenarios where this tool is preferred over siblings or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find notes with no connections to other notes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states what the tool does but lacks critical details: it doesn't specify if this is a read-only operation, what the output format is (e.g., list of note IDs or full content), whether it's resource-intensive, or if permissions are required. For a tool with zero annotation coverage, 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 a single, clear sentence: 'Find notes with no connections to other notes.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool. Every part of the sentence earns its place by defining the action and target.
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 lack of annotations and no output schema, the description is incomplete. It explains what the tool does but fails to address behavioral aspects like safety (e.g., is it read-only?), output format, or performance implications. For a tool in a note-taking system where operations might affect data integrity, more context is needed to ensure proper use.
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 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics beyond what the schema provides. A baseline score of 4 is appropriate as it doesn't introduce confusion or redundancy regarding parameters.
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: 'Find notes with no connections to other notes.' It uses a specific verb ('Find') and resource ('notes'), and the concept of 'orphaned notes' (notes with no connections) is well-defined. However, it doesn't explicitly differentiate from sibling tools like 'zk_find_similar_notes' or 'zk_find_central_notes' beyond the orphaned aspect.
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 no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is appropriate (e.g., for cleanup tasks, identifying isolated content) or when to use other tools like 'zk_search_notes' or 'zk_get_linked_notes' instead. There's no context about prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find notes similar to a given note. Args: note_id: ID of the reference note threshold: Similarity threshold (0.0-1.0) limit: Maximum number of results to return
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| note_id | Yes | ||
| threshold | No |
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 states the tool finds similar notes but doesn't explain what 'similar' means (e.g., content-based, tags, metadata), how similarity is computed, or any behavioral traits like performance considerations, rate limits, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its 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 highly concise and well-structured: a clear purpose statement followed by bullet-point parameter explanations. Every sentence earns its place, with no wasted words, and it's front-loaded with the core functionality. The formatting with 'Args:' enhances readability without verbosity.
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's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and parameters well but lacks details on behavior, output format, and usage context. Without annotations or output schema, more information on what the tool returns and how it operates would improve completeness for effective agent use.
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 description adds meaningful semantics beyond the input schema, which has 0% schema description coverage. It explains that 'note_id' is the 'ID of the reference note', 'threshold' is the 'Similarity threshold (0.0-1.0)', and 'limit' is the 'Maximum number of results to return'. This clarifies the purpose and constraints of each parameter, compensating well for the lack of 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 clearly states the tool's purpose: 'Find notes similar to a given note.' This specifies the verb ('find') and resource ('notes'), but doesn't explicitly differentiate it from sibling tools like 'zk_search_notes' or 'zk_get_linked_notes', which might also retrieve notes based on different criteria. It's clear but lacks sibling differentiation.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'zk_search_notes' (for keyword-based searches) or 'zk_get_linked_notes' (for explicitly linked notes), leaving the agent to infer usage context. There's no explicit when/when-not or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all tags in the Zettelkasten.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states what the tool does but doesn't disclose operational traits like whether it returns a paginated list, performance implications for large datasets, or any authentication requirements. The description is functional but lacks depth for agent decision-making.
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 a single, clear sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the essential information without unnecessary 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?
Given the tool's simplicity (no parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks context about output format (e.g., list structure, tag metadata) or behavioral nuances. For a read-only tool with zero inputs, this is the minimum viable description.
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 with 100% schema description coverage, so the schema fully documents the absence of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for zero-parameter tools, as no compensation is 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 clearly states the action ('Get all') and resource ('tags in the Zettelkasten'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'zk_search_notes' which might also involve tags, but the specificity of 'all tags' provides reasonable distinction.
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?
No guidance is provided on when to use this tool versus alternatives. While it's implied this retrieves all tags, there's no mention of whether to use this for bulk operations versus filtered searches via other tools, nor any prerequisites or constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get notes linked to/from a note. Args: note_id: ID of the note direction: Direction of links (outgoing, incoming, both)
| Name | Required | Description | Default |
|---|---|---|---|
| direction | No | both | |
| note_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only describes the basic operation without mentioning critical details like whether this is a read-only operation, what permissions are required, how results are formatted, or if there are rate limits. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 appropriately sized and front-loaded, with the core purpose stated first followed by parameter details. It uses minimal words to convey essential information, though the formatting with indentation could be slightly cleaner. Every sentence earns its place, making it efficient.
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 complexity of a tool that retrieves linked notes, the lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., list of notes, link details), error conditions, or behavioral constraints. This leaves the agent with insufficient information for reliable use.
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 description includes an 'Args' section that lists and briefly explains the two parameters ('note_id' and 'direction'), adding meaning beyond the input schema, which has 0% description coverage. However, it doesn't provide details like the format of 'note_id' or examples for 'direction' values, so it only partially compensates for the schema's lack of 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 clearly states the tool's purpose: 'Get notes linked to/from a note.' It specifies the verb ('Get') and resource ('notes linked to/from a note'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'zk_get_note' or 'zk_search_notes', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'zk_get_note' (for single notes) or 'zk_search_notes' (for broader searches), nor does it specify prerequisites or exclusions. This lack of context leaves the agent guessing about appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve a note by ID or title. Args: identifier: The ID or title of the note
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Retrieve') but fails to describe critical traits such as error handling (e.g., what happens if the note doesn't exist), permission requirements, rate limits, or the format of returned data. This leaves significant gaps for a read operation in a note-taking context.
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 appropriately sized and front-loaded with the core purpose in the first sentence, followed by a brief parameter explanation. It avoids unnecessary verbosity, though the formatting with indentation and 'Args:' could be slightly cleaner for direct readability.
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's complexity (a read operation with potential behavioral nuances), lack of annotations, no output schema, and minimal parameter details, the description is incomplete. It doesn't address return values, error cases, or interaction with sibling tools, making it inadequate for reliable agent use without additional context.
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 description adds minimal semantics beyond the input schema, which has 0% description coverage. It clarifies that 'identifier' can be an ID or title, but doesn't specify format (e.g., string patterns), uniqueness constraints, or examples. With one parameter and low schema coverage, this provides some value but doesn't fully compensate for the lack of schema details.
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 with a specific verb ('Retrieve') and resource ('a note'), and distinguishes it from siblings like 'zk_search_notes' or 'zk_list_notes_by_date' by focusing on direct lookup by ID or title. However, it doesn't explicitly contrast with 'zk_get_linked_notes' or 'zk_find_similar_notes', which slightly limits differentiation.
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 usage for retrieving notes by specific identifiers (ID or title), but provides no explicit guidance on when to use this versus alternatives like 'zk_search_notes' for broader queries or 'zk_list_notes_by_date' for date-based listing. It lacks clear exclusions or prerequisites, leaving usage context somewhat ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List notes created or updated within a date range. Args: start_date: Start date in ISO format (YYYY-MM-DD) end_date: End date in ISO format (YYYY-MM-DD) use_updated: Whether to use updated_at instead of created_at limit: Maximum number of results to return
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | No | ||
| limit | No | ||
| start_date | No | ||
| use_updated | No |
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. While it mentions the tool lists notes, it doesn't cover critical aspects like whether this is a read-only operation, how results are ordered, what happens if no notes match, or if there are rate limits. For a listing tool with zero annotation coverage, this leaves significant gaps in understanding its 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 efficiently structured: a clear purpose statement followed by a bullet-point list of parameters with brief explanations. Every sentence earns its place, and there's no redundant or verbose content, making it easy to parse quickly.
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's moderate complexity (4 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and parameters well but lacks behavioral details (e.g., ordering, pagination, error handling) and usage context relative to siblings. Without an output schema, it also doesn't describe return values, which is a notable gap for a listing tool.
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 description adds substantial value beyond the input schema, which has 0% description coverage. It explains all four parameters concisely: 'start_date' and 'end_date' as ISO format dates, 'use_updated' as a boolean for choosing between creation or update timestamps, and 'limit' as a maximum result count. This compensates well for the schema's lack of 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 clearly states the tool's purpose: 'List notes created or updated within a date range.' This specifies the verb ('list'), resource ('notes'), and scope ('within a date range'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'zk_search_notes' or 'zk_find_similar_notes', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. With siblings like 'zk_search_notes' and 'zk_find_similar_notes' available, there's no indication of when date-range filtering is preferred over other search methods, nor any mention of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rebuild the database index from files.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('rebuild') but doesn't explain critical traits: whether this is a destructive operation (e.g., overwrites existing index), requires specific permissions, has performance impacts (e.g., time-consuming), or provides any output/confirmation. This leaves significant gaps for a tool that likely involves system-level changes.
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 a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for a tool with no parameters.
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 complexity of a 'rebuild' operation (likely involving system changes), no annotations, and no output schema, the description is inadequate. It doesn't cover behavioral risks, expected outcomes, or error conditions, leaving the agent underinformed about how to use this tool safely and effectively.
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 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for 0 parameters is 4, as the description appropriately doesn't waste space on nonexistent parameters, though it could theoretically mention why no inputs are needed (e.g., 'rebuilds using all available files').
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 ('rebuild') and the target resource ('database index from files'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from its siblings (like zk_find_orphaned_notes or zk_find_similar_notes that might involve index operations), which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (e.g., when the index is corrupted), exclusions, or related tools (like zk_search_notes that might rely on the index), leaving the agent with no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Remove a link between two notes. Args: source_id: ID of the source note target_id: ID of the target note bidirectional: Whether to remove the link in both directions
| Name | Required | Description | Default |
|---|---|---|---|
| bidirectional | No | ||
| source_id | Yes | ||
| target_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Remove a link') but lacks critical details: whether this operation is reversible, if it requires specific permissions, what happens if the link doesn't exist (e.g., error or no-op), or any side effects on note metadata. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 in the first sentence, followed by a structured Args section. It avoids unnecessary fluff, but the Args formatting adds slight verbosity. Overall, it's efficient and well-organized, with each sentence contributing directly to understanding.
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's moderate complexity (mutation with 3 parameters), no annotations, and no output schema, the description is partially complete. It covers parameter meanings adequately but lacks behavioral context (e.g., error handling, effects) and output expectations. For a link removal operation, this leaves gaps that could hinder correct agent 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 description adds meaningful semantics for all three parameters beyond the schema, which has 0% description coverage. It explains that 'source_id' and 'target_id' are note IDs for the link endpoints, and clarifies 'bidirectional' as controlling removal direction. This compensates well for the schema's lack of descriptions, though it could note format expectations for IDs.
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 ('Remove a link') and resource ('between two notes'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'zk_delete_note' or 'zk_create_link' beyond the obvious verb difference, missing an opportunity to clarify its unique role in link management versus note deletion.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., that a link must exist), contrast with 'zk_create_link' for adding links, or explain scenarios where removal is appropriate (e.g., cleaning up outdated connections). This leaves the agent without contextual usage cues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for notes by text, tags, or type. Args: query: Text to search for in titles and content tags: Comma-separated list of tags to filter by note_type: Type of note to filter by limit: Maximum number of results to return
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| note_type | No | ||
| query | No | ||
| tags | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions search functionality but doesn't describe the return format (e.g., list of note objects), pagination behavior (implied by 'limit'), error conditions, or performance characteristics. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 appropriately sized and front-loaded with the core purpose in the first sentence. The parameter explanations are clear and efficient, though the formatting with 'Args:' and indentation is slightly verbose. Every sentence adds value, with no redundant 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?
Given 4 parameters with 0% schema coverage and no output schema, the description does a decent job explaining parameter semantics but falls short on behavioral aspects. It doesn't describe the return structure (critical for a search tool) or error handling. For a search operation with multiple siblings, more context about differences would be helpful.
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 description adds substantial value beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'query' searches titles and content, 'tags' is comma-separated, 'note_type' filters by type, and 'limit' sets maximum results. This compensates well for the schema's lack of descriptions, though it doesn't specify allowed note types or tag formats.
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: 'Search for notes by text, tags, or type.' This specifies the verb ('search') and resource ('notes') with three search dimensions. It distinguishes from siblings like 'zk_list_notes_by_date' (date-based listing) and 'zk_find_similar_notes' (similarity-based), though not explicitly named.
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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention when to prefer this over 'zk_find_similar_notes' (semantic similarity) or 'zk_list_notes_by_date' (date-based listing), nor does it specify prerequisites or exclusions. The description only states what it does, not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an existing note. Args: note_id: The ID of the note to update title: New title (optional) content: New content (optional) note_type: New note type (optional) tags: New comma-separated list of tags (optional)
| Name | Required | Description | Default |
|---|---|---|---|
| content | No | ||
| note_id | Yes | ||
| note_type | No | ||
| tags | No | ||
| title | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is an update operation (implying mutation), but doesn't describe what happens with partial updates, whether changes are reversible, what permissions are required, or what the response looks like. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.
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 efficiently structured with a clear purpose statement followed by parameter documentation. The parameter list is formatted for readability. While concise, the purpose statement could be slightly more specific about what 'update' entails beyond changing fields.
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 mutation tool with 5 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It covers the basic operation and parameters but lacks crucial context about behavioral traits, error conditions, return values, and usage guidelines relative to sibling tools.
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 description lists all 5 parameters with brief explanations, adding value beyond the schema which has 0% description coverage. It clarifies that 'note_id' is required and other parameters are optional, and provides format hints (e.g., 'comma-separated list' for tags). However, it doesn't explain parameter constraints, valid values, or interactions between parameters.
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 resource ('an existing note'), making the purpose immediately understandable. It distinguishes this from creation tools like 'zk_create_note' by specifying it updates existing notes, though it doesn't explicitly contrast with all sibling tools like 'zk_delete_note' or 'zk_get_note'.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing the note ID from a previous operation), when not to use it (e.g., for creating new notes), or how it differs from similar tools like 'zk_get_note' or 'zk_delete_note'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0zk_create_linkzk_create_notezk_delete_notezk_find_central_noteszk_find_orphaned_noteszk_find_similar_noteszk_get_all_tagszk_get_linked_noteszk_get_notezk_list_notes_by_datezk_rebuild_indexzk_remove_linkzk_search_noteszk_update_noteScored across 14 tools
Each tool has a clearly distinct purpose with no ambiguity. For example, zk_create_note, zk_update_note, and zk_delete_note handle different CRUD operations, while zk_find_central_notes, zk_find_orphaned_notes, and zk_find_similar_notes target specific query types. The link management tools (zk_create_link, zk_remove_link) are separate from note operations, ensuring agents can easily differentiate between them.
All tools follow a consistent 'zk_verb_noun' pattern with snake_case throughout. The verbs are descriptive and aligned with their functions (e.g., create, delete, get, list, find, search, update, rebuild). This uniformity makes the tool set predictable and easy for agents to navigate without confusion.
With 14 tools, this server is well-scoped for managing a Zettelkasten system. It covers essential operations like note CRUD, link management, and various query types (e.g., search, similarity, centrality). Each tool serves a specific purpose without redundancy, making the count appropriate for the domain's complexity and typical use cases.
The tool set provides complete coverage for Zettelkasten workflows, including full CRUD for notes and links, comprehensive querying (by text, tags, date, similarity, centrality, orphaned status), and maintenance (rebuild_index). There are no obvious gaps; agents can create, retrieve, update, delete, and analyze notes and their relationships seamlessly, supporting the entire knowledge management lifecycle.
Persistent memory layer for AI tools. Save and recall notes across Claude and other MCP clients.
MCP-native notes and memory for ChatGPT, Claude, and other AI tools.
Persistent memory for AI assistants. Save once; recall from Claude, ChatGPT, or any MCP client.
Search, read, and write your Apple Notes from ChatGPT/Claude via a local Mac agent + MCP relay.
