MCP Server Learning
This MCP server provides comprehensive flashcard management, Anki integration, and educational tools including Zotero and Obsidian integration, plus mathematical verification capabilities.
Flashcard Management:
Create flashcards from text with support for front-back, cloze deletion, and diagram card types
Full LaTeX math support for mathematical notation and formulas
Handle ASCII art, TikZ diagrams, and flowcharts
Generate HTML and text previews before uploading
Export flashcards in various formats
Anki Integration:
Upload flashcards directly to Anki with automatic LaTeX-to-MathJax conversion
Check connection, list available decks/models, and verify connectivity
Full CRUD operations: search, update, and delete notes
Sync collection with AnkiWeb for cloud backup
Custom deck naming, tagging, and batch operations
Integration via AnkiConnect addon with optional API key authentication
Zotero Integration:
Connect via Web API or local database
Search items, list collections, and get detailed item information
Generate flashcards directly from Zotero items or collections
Create new items, add notes, and manage collections
Access templates for various item types
Obsidian Integration:
Connect to vaults and interact with markdown notes
Search by content, title, or tags; find backlinks and orphaned notes
Extract structured content: headers, paragraphs, lists, quotes, and code blocks
Generate flashcards from Obsidian notes
Get vault statistics and note details
Mathematical Verification:
Verify single mathematical steps and multi-step proofs
Check derivatives, integrals (definite and indefinite), limits, and equalities
Simplify expressions with optional intermediate steps
Verify expression equivalence and mathematical identities
Native LaTeX notation support as input
Enables direct upload of flashcards to Anki using AnkiConnect addon, with support for multiple card types (front-back, cloze, diagram), deck management, and connection verification.
Supports generating LaTeX-formatted flashcards with mathematical notation and provides native LaTeX input for mathematical expressions in verification tools.
Enables interaction with Obsidian vaults including searching notes, extracting headers and blocks, managing tags, analyzing backlinks, generating vault statistics, and creating flashcards from note content.
Powers mathematical verification capabilities including step-by-step proof verification, expression simplification, identity checking, and operations for calculus and linear algebra.
Provides tools for searching items, managing collections, retrieving notes, creating new items, and generating flashcards from Zotero library content using either Web API or local database access.
Click on "Install 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., "@MCP Server Learningcreate flashcards from my Zotero collection on calculus"
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.
MCP Server Learning
A Model Context Protocol (MCP) server designed to help with learning and educational tasks. Includes flashcard generation, Zotero integration, Obsidian connectivity, and mathematical verification tools.
Features
Flashcard Server
The flashcard server provides tools for creating, managing, and exporting flashcards in various formats:
Multiple Card Types: Support for front-back cards, cloze deletion cards, and diagram cards
LaTeX Support: Generate LaTeX-formatted flashcards with mathematical notation support
Anki Integration: Direct upload to Anki using AnkiConnect addon
HTML Preview: Generate beautiful HTML previews of your flashcards
Diagram Support: Handle ASCII art, TikZ diagrams, and flowcharts
Mathematical Verification Server
A comprehensive server for verifying mathematical expressions and proofs:
LaTeX Input: Native support for LaTeX mathematical notation
Multi-Step Proof Verification: Verify complete proofs step-by-step
Calculus Support: Derivatives, integrals, limits, and series
Linear Algebra: Matrix operations and vector spaces
Expression Simplification: Automatically simplify complex expressions with step-by-step explanations
Identity Checking: Verify mathematical identities (Pythagorean, trigonometric, etc.)
Related MCP server: Anki MCP Server
Installation
This project uses uv for dependency management.
# Clone the repository
git clone <repository-url>
cd mcp-server-learning
# Install dependencies
uv sync
# Install in development mode
uv pip install -e .Usage
Running the Servers
FastMCP entrypoints are provided for each server:
# Flashcards / Anki
uv run fastmcp-flashcard-server
# Zotero
export ZOTERO_API_KEY=... \
ZOTERO_LIBRARY_ID=... \
ZOTERO_LIBRARY_TYPE=user # or group
uv run fastmcp-zotero-server
# Obsidian
export OBSIDIAN_VAULT_PATH="/path/to/your/Obsidian/Vault"
uv run fastmcp-obsidian-server
# Mathematical Verification
uv run fastmcp-math-serverAvailable Tools
create_flashcards
Convert text content into LaTeX flashcards.
Parameters:
content(required): Text content to convertcard_type: "front-back" or "cloze" (default: "front-back")title: Title for the deck (default: "Flashcards")full_document: Whether to return complete LaTeX document (default: true)
Example input formats:
Q: What is the capital of France?
A: Paris
---
What is 2 + 2?
4For cloze cards:
The capital of {{France}} is {{Paris}}.create_single_card
Create a single flashcard.
Parameters:
For front-back cards:
front,backFor cloze cards:
cloze_textcard_type: "front-back" or "cloze"
create_diagram_card
Create a flashcard with a diagram.
Parameters:
diagram: The diagram contentexplanation: Explanation of the diagramdiagram_type: "ascii", "tikz", or "flowchart"
upload_to_anki
Upload flashcards directly to Anki.
Parameters:
content: Content to convert and uploadcard_type: Type of cards to createdeck_name: Anki deck name (default: "MCP Generated Cards")tags: Array of tags to addanki_api_key: Optional API key for AnkiConnect
check_anki_connection
Check connection to Anki and list available decks/models.
preview_cards
Generate HTML preview of flashcards.
Parameters:
content: Content to previewcard_type: Type of cardstitle: Preview titletags: Tags to display
Zotero Integration Tools
connect_zotero
Connect to your Zotero library using Web API or local database.
Parameters:
api_key: Zotero Web API key (optional for local access)user_id: Your Zotero user ID for personal librarygroup_id: Zotero group ID for group librarylocal_profile_path: Path to local Zotero profile (optional)prefer_local: Whether to prefer local database over Web API
search_zotero
Search items in your connected Zotero library.
Parameters:
query: Search termslimit: Maximum number of results (default: 20)
get_zotero_collections
List all collections in your Zotero library.
create_flashcards_from_zotero
Generate flashcards from Zotero items or collections.
Parameters:
item_keys: Specific Zotero item keys to usecollection_id: Collection ID to generate cards fromcard_types: Types of cards to create (citation,summary,definition)citation_style: Citation format (apa)
Zotero MCP Server
A FastMCP server for interacting with Zotero libraries using the pyzotero package.
Available Tools
search_zotero_items
Search for items in your Zotero library.
Parameters:
query(required): Search termslimit: Maximum results (default: 50)item_type: Filter by type (e.g., 'book', 'journalArticle')
get_zotero_item
Get detailed information about a specific item.
Parameters:
item_key(required): The Zotero item key
get_item_notes
Get all notes associated with an item.
Parameters:
item_key(required): The Zotero item key
list_zotero_collections
List all collections in the library.
get_collection_items
Get items from a specific collection.
Parameters:
collection_key(required): The collection keylimit: Maximum results (default: 50)
create_zotero_item
Create a new item in the library.
Parameters:
item_type(required): Type of item ('book', 'journalArticle', etc.)title(required): Item titlecreators: Array of creator objectsdate: Publication dateurl: Item URLabstract: Abstract/summarytags: Array of tag stringsextra_fields: Additional type-specific fields
create_item_note
Add a note to an existing item.
Parameters:
parent_item_key(required): Key of the parent itemnote_content(required): HTML-formatted note content
add_item_to_collection
Add an item to a collection.
Parameters:
item_key(required): The item keycollection_key(required): The collection key
get_item_templates
Get templates for creating different item types.
Obsidian MCP Server
A FastMCP server for interacting with Obsidian vaults and markdown notes.
Available Tools
get_vault_stats
Get comprehensive statistics about your Obsidian vault.
list_vault_notes
List all notes in the vault with optional pagination.
Parameters:
limit: Maximum number of notes to returnoffset: Number of notes to skip (for pagination)refresh_cache: Whether to refresh the note cache
search_obsidian_notes
Search for notes by content, title, or tags.
Parameters:
query(required): Search termssearch_in: Fields to search in (content, title, tags)limit: Maximum number of results
get_obsidian_note
Get detailed information about a specific note.
Parameters:
note_name(required): Name of the note (without .md extension)
get_notes_by_tag
Get all notes that have a specific tag.
Parameters:
tag(required): Tag to search for
get_note_backlinks
Find all notes that link to a specific note.
Parameters:
note_name(required): Name of the note to find backlinks for
get_orphaned_notes
Find notes that have no incoming or outgoing links.
get_note_links
Get all wikilinks from a specific note.
Parameters:
note_name(required): Name of the note to get links from
extract_note_headers
Extract structured headers from a note.
Parameters:
note_name(required): Name of the note to extract headers from
extract_note_blocks
Extract content blocks (paragraphs, lists, quotes, code) from a note.
Parameters:
note_name(required): Name of the note to extract blocks fromblock_types: Types of blocks to extract (paragraph, list, quote, code, header)
get_notes_for_flashcards
Extract content from notes that is suitable for flashcard generation.
Parameters:
note_names: Names of specific notes to processtag_filter: Only process notes with this tagcontent_types: Types of content to extract (headers, definitions, lists, quotes)
Obsidian Integration Tools
connect_obsidian
Connect to an Obsidian vault.
Parameters:
vault_path: Path to your Obsidian vault directory
search_obsidian
Search notes in your connected Obsidian vault.
Parameters:
query: Search termssearch_in: Fields to search (content,title,tags)limit: Maximum number of results (default: 20)
get_obsidian_vault_stats
Get statistics about your Obsidian vault (note count, tags, etc.).
create_flashcards_from_obsidian
Generate flashcards from Obsidian notes.
Parameters:
note_names: Specific note names to processtag_filter: Only process notes with this tagcontent_types: Types of content to extract (headers,definitions,lists,quotes)card_type: Type of flashcards to generate (front-back,cloze)
Mathematical Verification MCP Server
A FastMCP server for verifying mathematical expressions and multi-step proofs using SymPy. Focused on calculus, analysis, and linear algebra with LaTeX input support.
Available Tools
verify_step
Verify a single mathematical step with proper justification.
Parameters:
expression(required): Mathematical expression in LaTeX format (e.g.,\frac{d}{dx}(x^2))expected_result(required): Expected result in LaTeX format (e.g.,2x)assumptions: Optional list of assumptions (e.g.,["x is real"])operation: Type of operation -"equality","derivative","integral", or"limit"
Examples:
verify_step("x^2 + 2x + 1", "(x+1)^2", operation="equality")
verify_step("x^2", "2x", operation="derivative")verify_proof
Verify a multi-step mathematical proof.
Parameters:
steps(required): List of proof steps. Each step should contain:expression: The mathematical expression (LaTeX)justification: Reason for this stepresult(optional): Expected result after this step
assumptions: Optional list of assumptions about variables
Example:
steps = [
{
"expression": r"\int x dx",
"result": r"\frac{x^2}{2} + C",
"justification": "Power rule for integration"
},
{
"expression": r"\frac{d}{dx}(\frac{x^2}{2} + C)",
"result": "x",
"justification": "Differentiate with respect to x"
}
]simplify_expression
Simplify a mathematical expression and optionally show steps.
Parameters:
expression(required): Mathematical expression in LaTeX formatshow_steps: Whether to show intermediate simplification steps (default:true)
Example:
simplify_expression(r"\frac{x^2 - 1}{x - 1}", show_steps=True)verify_equivalence
Verify if two mathematical expressions are equivalent.
Parameters:
expr1(required): First expression in LaTeX formatexpr2(required): Second expression in LaTeX formatassumptions: Optional list of assumptions about variables
Example:
verify_equivalence("x^2 - 1", "(x-1)(x+1)")check_identity
Check if a mathematical identity holds.
Parameters:
identity_expr(required): Identity to check (e.g.,sin(x)^2 + cos(x)^2 - 1)variable: Variable in the identity (default:'x')test_values: Optional list of specific values to test
Example:
check_identity(r"\sin^2(x) + \cos^2(x) - 1", variable="x")verify_derivative
Verify a derivative calculation.
Parameters:
expression(required): Expression to differentiate (LaTeX format)variable(required): Variable to differentiate with respect toexpected_derivative(required): Expected derivative result (LaTeX format)
Example:
verify_derivative(r"\sin(x) \cos(x)", "x", r"\cos^2(x) - \sin^2(x)")verify_integral
Verify an integral calculation.
Parameters:
expression(required): Expression to integrate (LaTeX format)variable(required): Variable to integrate with respect toexpected_integral(required): Expected integral result (LaTeX format)is_definite: Whether this is a definite integral (default:false)lower_limit: Lower limit for definite integral (LaTeX format or number)upper_limit: Upper limit for definite integral (LaTeX format or number)
Examples:
verify_integral("x", "x", r"\frac{x^2}{2} + C", is_definite=False)
verify_integral("x", "x", r"\frac{1}{2}", is_definite=True, lower_limit="0", upper_limit="1")LaTeX Input Format
The server accepts standard LaTeX mathematical notation:
Inline math:
x^2,\frac{1}{2},\sin(x)Functions:
\sin(x),\cos(x),\ln(x),\exp(x)Fractions:
\frac{numerator}{denominator}Powers:
x^2,e^{x}Greek letters:
\alpha,\beta,\piSpecial symbols:
\cdot(multiplication),\int(integral),\frac{d}{dx}(derivative)
Integration Setup
Anki Integration
To use Anki integration:
Install the AnkiConnect addon in Anki
Start Anki with the addon enabled
Use the
upload_to_ankiorcheck_anki_connectiontools
Zotero Integration
To use Zotero integration:
Option 1: Local Database (Recommended)
Install Zotero desktop application
Use
connect_zoterotool with local accessNo additional setup required
Option 2: Web API
Get your Zotero API key from zotero.org/settings/keys
Find your user ID from your Zotero profile URL
Use
connect_zoterotool with API credentials
Obsidian Integration
To use Obsidian integration:
Locate your Obsidian vault directory
Use
connect_obsidiantool with the vault pathThe connector will scan and index your notes automatically
Development
Running Tests
uv run pytestCode Formatting
uv run black src/
uv run isort src/Type Checking
uv run mypy src/Configuration with Claude Desktop
Flashcard Server
Add to your Claude Desktop configuration:
{
"mcpServers": {
"learning": {
"command": "uv",
"args": ["run", "fastmcp-flashcard-server"],
"cwd": "/path/to/mcp-server-learning"
}
}
}Zotero Server
Configure the Zotero FastMCP server:
{
"mcpServers": {
"zotero": {
"command": "uv",
"args": ["run", "fastmcp-zotero-server"],
"cwd": "/path/to/mcp-server-learning",
"env": {
"ZOTERO_API_KEY": "your-zotero-api-key",
"ZOTERO_LIBRARY_ID": "your-library-id",
"ZOTERO_LIBRARY_TYPE": "user"
}
}
}
}Obsidian Server
Configure the Obsidian FastMCP server:
{
"mcpServers": {
"obsidian": {
"command": "uv",
"args": ["run", "fastmcp-obsidian-server"],
"cwd": "/path/to/mcp-server-learning",
"env": {
"OBSIDIAN_VAULT_PATH": "/absolute/path/to/your/Obsidian/Vault"
}
}
}
}Zotero Environment Variables
You need to set these environment variables for the Zotero server:
ZOTERO_API_KEY: Your Zotero Web API key (get from zotero.org/settings/keys)
ZOTERO_LIBRARY_ID: Your Zotero library ID
For personal library: Your user ID (find in your Zotero profile URL)
For group library: The group ID
ZOTERO_LIBRARY_TYPE: Either "user" for personal library or "group" for group library
Getting Your Zotero Credentials
API Key: Go to zotero.org/settings/keys and create a new private key
User ID: Go to zotero.org/settings/keys - your user ID is shown at the top
Group ID: For group libraries, the ID is in the group's URL on zotero.org
Obsidian Server
For the standalone Obsidian server, add this to your Claude Desktop configuration:
{
"mcpServers": {
"obsidian": {
"command": "uv",
"args": ["run", "obsidian-mcp-server"],
"cwd": "/path/to/mcp-server-learning",
"env": {
"OBSIDIAN_VAULT_PATH": "/path/to/your/obsidian/vault"
}
}
}
}Obsidian Environment Variable
You need to set this environment variable for the Obsidian server:
OBSIDIAN_VAULT_PATH: Full path to your Obsidian vault directory (the folder containing your .md files and .obsidian folder)
Mathematical Verification Server
Configure the math verification server in Claude Desktop:
{
"mcpServers": {
"math-verification": {
"command": "uv",
"args": ["run", "fastmcp-math-server"],
"cwd": "/path/to/mcp-server-learning"
}
}
}This server requires no environment variables - it's ready to use immediately after installation.
Configuration with ChatGPT Desktop
ChatGPT Desktop requires a remote HTTPS MCP endpoint. Local MCP servers are not supported directly, so run the suite on your machine and expose it publicly through a tunnel or cloud deployment.
Choose your exposure mode
Option | Setup effort | URL stability | Best for |
ngrok quick tunnel | Very low | Changes on restart | Fast testing |
ngrok reserved domain (paid) | Low | Stable | Simple managed stable URL |
Cloudflare quick tunnel | Low | Changes on restart | Fast trial without DNS setup |
Cloudflare named tunnel + DNS | Medium (one-time) | Stable | Daily use (recommended) |
Always-on cloud host (Railway/Fly/Render/VPS) | Medium-high | Stable | Endpoint stays up when laptop is off |
Recommended: Cloudflare named tunnel + custom subdomain
Set environment variables if you plan to use Zotero or Obsidian:
Zotero:
ZOTERO_API_KEY,ZOTERO_LIBRARY_ID,ZOTERO_LIBRARY_TYPEObsidian:
OBSIDIAN_VAULT_PATH
Run the suite locally on port 8000:
./scripts/run_learning_suite.shInstall and authenticate cloudflared:
brew install cloudflared
cloudflared tunnel loginCreate a named tunnel:
cloudflared tunnel create learning-suite-mcpCreate Cloudflare config from template and fill placeholders:
mkdir -p ~/.cloudflared
cp scripts/cloudflared-config.example.yml ~/.cloudflared/config.ymlCreate DNS route to a stable hostname:
cloudflared tunnel route dns learning-suite-mcp mcp.<your-domain>Run the tunnel:
cloudflared tunnel --config ~/.cloudflared/config.yml run learning-suite-mcpMake tunnel persistent across reboots:
sudo cloudflared service installValidate the public endpoint:
./scripts/verify_remote_mcp.sh https://mcp.<your-domain>/mcp/ChatGPT Desktop MCP settings
Add a new MCP server in ChatGPT Desktop:
URL:
https://mcp.<your-domain>/mcp/Auth:
NoneName:
learning-suite(or any label you prefer)
Use the trailing slash in the ChatGPT URL to avoid an extra redirect hop.
Tools are namespaced by prefix: flashcard_*, zotero_*, obsidian_*, math_*.
Quick alternatives
ngrok quick tunnel
./scripts/run_learning_suite.sh
ngrok http 8000Use: https://<ngrok-host>/mcp/
ngrok reserved domain (paid)
ngrok http --url=<reserved-subdomain>.ngrok.app 8000Use: https://<reserved-subdomain>.ngrok.app/mcp/
Cloudflare quick tunnel (ephemeral URL)
cloudflared tunnel --url http://localhost:8000Use: https://<random>.trycloudflare.com/mcp/
Verification checklist
Local server starts and logs
Starting Learning MCP Suite ... /mcp.Remote URL responds through the tunnel (
verify_remote_mcp.shsucceeds).ChatGPT Desktop lists prefixed tools (
flashcard_*,zotero_*,obsidian_*,math_*).Stopping local server causes expected failure; restarting restores tool access.
Contributing
Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Usage Examples
Basic Flashcard Creation
# Create flashcards from text
create_flashcards(content="Q: What is Python? A: A programming language", card_type="front-back")
# Create cloze deletion cards
create_flashcards(content="Python is a {{programming language}} used for {{web development}}.", card_type="cloze")Future Features
Advanced citation styles (MLA, Chicago, IEEE)
Batch processing for large collections
Spaced repetition scheduling integration
Cross-reference detection between Zotero and Obsidian
Support for multimedia flashcards
Export to other flashcard platforms
Collaborative learning features
Available Tools
8 toolscheck_anki_connectionC
Check connection to Anki and get available decks/models
Args: anki_api_key: Optional AnkiConnect API key
| Name | Required | Description | Default |
|---|---|---|---|
| anki_api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions checking connection and getting decks/models, but lacks details on behavioral traits: it doesn't specify what 'check connection' entails (e.g., ping, authentication), what happens on failure, whether it's read-only (implied but not stated), or any rate limits. The description is minimal and misses key operational 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: the first sentence states the core purpose clearly. The 'Args:' section adds necessary parameter context without redundancy. However, the structure could be more integrated (e.g., merging description and args), and there's minor room for trimming (e.g., 'Optional' is implied by schema). Overall efficient but not perfect.
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 1 parameter, no annotations, and an output schema exists, the description is moderately complete. It covers the basic purpose and parameter hint, but lacks context on usage guidelines, behavioral details, or integration with siblings. The output schema likely handles return values, so that gap is mitigated. However, for a connection-checking tool, more operational context would be beneficial.
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. It adds some meaning by explaining 'anki_api_key' as 'Optional AnkiConnect API key', which clarifies its purpose beyond the schema's title 'Anki Api Key'. However, with 1 parameter and low coverage, it doesn't fully compensate—e.g., it doesn't explain format or when to provide the key. Baseline adjusted upward slightly due to minimal param info.
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: 'Check connection to Anki and get available decks/models'. It uses specific verbs ('check', 'get') and identifies the resource (Anki connection, decks/models). However, it doesn't explicitly differentiate from sibling tools like 'sync_anki' which might also involve connection checking, so it misses full 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 prerequisites (e.g., use this first to verify connectivity before other operations) or contrast with siblings like 'sync_anki' or 'search_anki_notes'. The only implied usage is checking connection, but no explicit when/when-not instructions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_flashcardsA
Convert text into flashcards with LaTeX math rendering for Claude Desktop
Args: content: Text content to convert to flashcards. Use Q: A: format or separate lines. card_type: Type of flashcard - "front-back" or "cloze"
Examples: Basic card: Q: What is the sigmoid function? A: $\sigma(x) = \frac{1}{1 + e^{-x}}$
Markov's inequality:
Q: What is Markov's inequality?
A: For any non-negative random variable X and constant a > 0: $$P(X \geq a) \leq \frac{E[X]}{a}$$
Cloze card (use card_type="cloze"):
The probability formula is {{P(X ≥ a) ≤ E[X]/a}}| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | ||
| card_type | No | front-back |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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 mentions LaTeX rendering and format requirements, but doesn't cover important aspects like whether this creates persistent flashcards, requires Anki connectivity, has rate limits, or what happens with invalid input. For a tool with no 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 well-structured with clear sections (purpose, args, examples) and efficiently uses space. The examples are helpful but somewhat lengthy. Every sentence earns its place, though it could be slightly more concise in the example section while maintaining clarity.
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 an output schema (which handles return values), 2 parameters with 0% schema coverage, and no annotations, the description does well by thoroughly documenting parameters and providing practical examples. However, it could better address the tool's relationship to Anki (given sibling tools) and clarify whether this creates persistent flashcards or just formats them.
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 excellent parameter semantics beyond the schema. With 0% schema description coverage, it fully compensates by explaining both parameters: 'content' gets detailed format examples (Q:A format, separate lines), and 'card_type' gets clear explanations of the two valid values with specific usage examples. This adds substantial value beyond the bare 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: 'Convert text into flashcards with LaTeX math rendering for Claude Desktop'. It specifies the verb ('convert'), resource ('text into flashcards'), and a key feature ('LaTeX math rendering'). However, it doesn't explicitly differentiate from sibling tools like 'preview_cards' or 'upload_to_anki', which might have overlapping functionality.
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 implied usage guidance through examples showing when to use 'front-back' vs 'cloze' card types, but lacks explicit when-to-use statements or comparisons with alternatives like 'preview_cards' or 'upload_to_anki'. The examples help but don't constitute formal guidelines about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_anki_notesC
Delete notes from Anki collection
Args: note_ids: List of note IDs to delete anki_api_key: Optional AnkiConnect API key
| Name | Required | Description | Default |
|---|---|---|---|
| note_ids | Yes | ||
| anki_api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action ('Delete') but lacks critical behavioral details: it doesn't specify if deletion is permanent, requires confirmation, affects associated cards, or has side effects. This is inadequate for a destructive operation with zero annotation coverage.
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 parameter hints in a structured 'Args:' section. It's efficient with minimal waste, though the parameter explanations could be more integrated into the flow.
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 a destructive tool with no annotations, 2 parameters at 0% schema coverage, and an output schema (which reduces need to describe returns), the description is partially complete. It covers the basics but misses critical context like deletion permanence, error handling, and integration with 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?
Schema description coverage is 0%, so the description must compensate. It adds basic semantics: 'note_ids' is a list of IDs to delete, and 'anki_api_key' is optional for AnkiConnect. However, it doesn't explain ID formats, sourcing, or API key usage, leaving gaps for the 2 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 ('Delete') and resource ('notes from Anki collection'), making the purpose unambiguous. It distinguishes from siblings like 'update_anki_note' by specifying deletion rather than modification, though it doesn't explicitly contrast with all alternatives like 'search_anki_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?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention prerequisites (e.g., needing note IDs from search results) or warn against misuse (e.g., irreversible deletion). The context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
preview_cardsB
Generate text preview of flashcards with LaTeX rendering for Claude Desktop
Args: content: Text content to convert to flashcards and preview card_type: Type of flashcard - "front-back" or "cloze" title: Title for the preview document tags: Tags to display on the cards (currently not shown in text format)
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | ||
| card_type | No | front-back | |
| title | No | Flashcard Preview | |
| tags | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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 LaTeX rendering and that tags are 'currently not shown in text format,' which adds some context. However, it lacks critical details: whether this is a read-only operation, if it modifies data, what the output looks like (though an output schema exists), or any performance/rate limits. For a tool with no annotations, this is insufficient.
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. It starts with a clear purpose statement, followed by a bulleted list of parameters with helpful explanations. Each sentence adds value, and there's no redundancy. However, the 'Args:' section could be integrated more smoothly into the flow.
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 an output schema, the description does a fair job. It explains parameters well but lacks behavioral context (e.g., no annotations, no mention of side effects). The output schema mitigates the need to describe return values, but for a tool with no annotations and multiple parameters, more guidance on usage and behavior 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. It adds meaningful semantics beyond the schema: explaining that 'content' is 'Text content to convert to flashcards and preview,' 'card_type' has options 'front-back' or 'cloze,' 'title' is for the 'preview document,' and 'tags' are 'Tags to display on the cards (currently not shown in text format).' This clarifies purpose and constraints, though it could detail format expectations for 'content.'
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: 'Generate text preview of flashcards with LaTeX rendering for Claude Desktop.' It specifies the verb ('Generate'), resource ('text preview of flashcards'), and key capability ('LaTeX rendering'). However, it doesn't explicitly differentiate from sibling tools like 'create_flashcards' or 'upload_to_anki' beyond the 'preview' 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 preview is needed (e.g., before creating actual flashcards), nor does it reference sibling tools like 'create_flashcards' or 'upload_to_anki' for comparison. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_anki_notesC
Search for notes in Anki collection
Args: query: Search query (e.g., 'deck:Math', 'tag:chemistry', 'Python programming') limit: Maximum number of notes to return anki_api_key: Optional AnkiConnect API key
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| limit | No | ||
| anki_api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions searching but doesn't disclose behavioral traits like whether this is a read-only operation, what happens with invalid queries, if there's pagination beyond the limit, or authentication requirements beyond the optional API key. The description is minimal and lacks operational 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. The Args section is structured but could be more integrated; overall, it's efficient with minimal waste, though the formatting as a code block might reduce readability in some contexts.
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 3 parameters with 0% schema coverage and no annotations, the description partially compensates with query examples and optional key note. An output schema exists, so return values needn't be explained. However, for a search tool with siblings, more context on behavior and usage would improve completeness, leaving it at an adequate but gapped level.
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. It adds value by providing query examples ('deck:Math', 'tag:chemistry', 'Python programming') and noting the API key is optional, which clarifies usage beyond the bare schema. However, it doesn't explain the limit's default or the API key's purpose, leaving some parameter semantics unclear.
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 'Search' and resource 'notes in Anki collection', making the purpose immediately understandable. However, it doesn't explicitly differentiate this search tool from other sibling tools like 'preview_cards' or 'check_anki_connection', which might also involve note retrieval or inspection.
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 'preview_cards' (which might show card representations) and 'check_anki_connection' (which might verify connectivity), there's no indication of when search is preferred, nor any prerequisites or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sync_ankiC
Synchronize Anki collection with AnkiWeb
Args: anki_api_key: Optional AnkiConnect API key
| Name | Required | Description | Default |
|---|---|---|---|
| anki_api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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. While 'Synchronize' implies a two-way data transfer operation, the description doesn't disclose what gets synchronized (decks, media, progress), whether this requires authentication beyond the API key, potential side effects, or what the synchronization process entails. It mentions the API key parameter but provides minimal behavioral 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 brief with two sentences: one stating the purpose and one documenting the parameter. It's front-loaded with the core functionality. The 'Args' section is efficiently formatted, though it could be integrated more smoothly into the narrative flow.
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 there's an output schema (which handles return values), no annotations, and only one parameter with 0% schema coverage, the description is minimally adequate. It states what the tool does and documents the parameter, but doesn't provide enough context about the synchronization behavior, authentication requirements, or relationship to sibling tools. For a synchronization operation, more behavioral context 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 includes an 'Args' section that documents the single parameter (anki_api_key) and indicates it's optional. However, with 0% schema description coverage, the schema provides no parameter documentation. The description compensates somewhat by documenting the parameter, but doesn't explain what the API key is used for, format requirements, or what happens when it's null. Baseline 3 is appropriate given the single parameter is documented.
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 ('Synchronize') and target resource ('Anki collection with AnkiWeb'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate this synchronization tool from sibling tools like 'upload_to_anki' or 'check_anki_connection', which might have overlapping functionality.
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 are multiple sibling tools (upload_to_anki, check_anki_connection) that might be related to synchronization or connectivity, but the description offers no context about prerequisites, timing, or when this specific synchronization is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_anki_noteB
Update an existing Anki note
Args: note_id: ID of the note to update fields: Fields to update (field_name: new_value) tags: New tags for the note (optional) anki_api_key: Optional AnkiConnect API key
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes | ||
| fields | Yes | ||
| tags | No | ||
| anki_api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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 cover critical aspects like required permissions, whether changes are reversible, rate limits, or error handling. It mentions an optional API key but doesn't explain authentication needs or defaults.
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 parameter list. However, the 'Args:' section is somewhat redundant with the schema and could be more integrated. It's concise but misses opportunities to embed usage context within the flow, making it slightly less 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 complexity (4 parameters, nested objects, mutation operation) and no annotations, the description is moderately complete. It covers parameters well but lacks behavioral context (e.g., side effects, error cases). The presence of an output schema reduces the need to explain return values, but overall it's adequate with clear gaps for a mutation 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?
Schema description coverage is 0%, so the description must compensate. It provides meaningful semantics for all 4 parameters: 'note_id' as the ID to update, 'fields' as field_name:new_value mappings, 'tags' as new tags (optional), and 'anki_api_key' as an optional API key. This adds clarity beyond the bare schema, though it lacks format details (e.g., tag array structure).
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 Anki note'), making the purpose immediately understandable. It distinguishes from siblings like 'create_flashcards' and 'delete_anki_notes' by focusing on modification rather than creation or deletion. However, it doesn't explicitly differentiate from potential similar tools like 'upload_to_anki' in terms of scope.
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 prerequisites (e.g., needing an existing note ID), exclusions, or comparisons to siblings like 'upload_to_anki' or 'search_anki_notes'. The description only lists parameters without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
upload_to_ankiA
Upload generated flashcards directly to Anki with proper MathJax conversion
Args: content: Text content to convert and upload to Anki (LaTeX will be converted to MathJax format) deck_name: Name of the Anki deck to upload to card_type: Type of flashcard - "front-back" or "cloze" tags: Tags to add to the cards anki_api_key: Optional AnkiConnect API key
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | ||
| deck_name | No | MCP Generated Cards | |
| card_type | No | front-back | |
| tags | No | ||
| anki_api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions 'upload' and 'convert', implying mutation, but doesn't disclose permissions needed, rate limits, error handling, or whether it creates new cards vs. updates existing ones. The description adds some context (MathJax conversion) but misses key operational traits for a write 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 well-structured with a front-loaded purpose statement followed by a parameter list. Each sentence earns its place by clarifying tool function and parameters. It could be slightly more concise by integrating parameter details into the main text, 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 5 parameters with 0% schema coverage, no annotations, and an output schema (which reduces need to describe returns), the description does a fair job. It covers parameter purposes and tool function but lacks behavioral context (e.g., side effects, error cases) and doesn't fully address when to use vs. siblings. For a mutation tool with rich siblings, it's adequate but has gaps.
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. It provides clear semantics for all 5 parameters: 'content' (text with LaTeX conversion), 'deck_name' (target deck), 'card_type' (options explained), 'tags' (purpose stated), and 'anki_api_key' (optional API key). This adds substantial meaning beyond the bare schema, though it doesn't detail format constraints (e.g., LaTeX syntax).
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 specific verbs ('upload', 'convert') and resources ('flashcards', 'Anki'), and distinguishes it from siblings like 'create_flashcards' (which likely generates but doesn't upload) and 'preview_cards' (which only shows). The mention of 'MathJax conversion' adds specificity beyond basic upload functionality.
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 context by specifying 'generated flashcards' and mentioning LaTeX conversion, suggesting it's for post-creation upload. However, it doesn't explicitly state when to use this vs. alternatives like 'create_flashcards' (which might handle generation) or 'update_anki_note' (for modifications), nor does it mention prerequisites like AnkiConnect setup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose with no ambiguity. Tools like check_anki_connection, create_flashcards, delete_anki_notes, search_anki_notes, sync_anki, update_anki_note, upload_to_anki, and preview_cards all target specific, non-overlapping operations in the Anki flashcard management workflow.
All tools follow a consistent verb_noun pattern with snake_case naming. Examples include check_anki_connection, create_flashcards, delete_anki_notes, search_anki_notes, sync_anki, update_anki_note, upload_to_anki, and preview_cards, showing perfect consistency throughout.
With 8 tools, this server is well-scoped for managing Anki flashcards. Each tool serves a clear purpose in the workflow, from connection checking and flashcard creation to search, update, deletion, syncing, and previewing, making the count appropriate and efficient.
The tool set provides complete CRUD/lifecycle coverage for Anki flashcard management. It includes creation (create_flashcards, upload_to_anki), reading/searching (search_anki_notes, preview_cards), updating (update_anki_note), deletion (delete_anki_notes), and auxiliary operations (check_anki_connection, sync_anki), with no obvious gaps.
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