Anki MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes: one retrieves cards with a specific tag, while the other adds a tag to cards. There is no overlap in functionality or ambiguity between them.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern (get_leech_cards, tag_reviewed_cards) with clear actions and objects, making them predictable and easy to understand.
Tool Count2/5With only 2 tools, the server feels thin for an Anki integration, lacking basic operations like creating cards, reviewing cards, or managing decks. This minimal set limits agent workflows significantly.
Completeness2/5The toolset is severely incomplete for an Anki server, missing core CRUD operations for cards, decks, and reviews. Agents cannot perform essential tasks like adding new cards or scheduling reviews, leading to dead ends.
Average 3/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
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 implies a mutation ('Add'), but doesn't clarify if this is destructive (e.g., overwrites existing tags), requires authentication, has side effects, or includes error handling. The description is minimal and lacks critical behavioral details for a write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's mutation nature (adding tags), no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, side effects, or return values, leaving significant gaps for an AI agent to understand how to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters (card_ids and custom_tag_prefix). The description adds no additional parameter semantics beyond implying the tag format ('reviewed on date'), which is already suggested by the schema's default value for custom_tag_prefix. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add') and target resource ('tag to specified cards'), with the specific tag content 'reviewed on date' mentioned. However, it doesn't differentiate from the sibling tool 'get_leech_cards' (which presumably retrieves rather than modifies cards), so it doesn't fully distinguish from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. The description doesn't mention the sibling tool 'get_leech_cards' or any other context for selection, nor does it specify prerequisites like required permissions or system states.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Retrieve' implies a read operation, it doesn't specify whether this requires authentication, how results are returned (format, pagination), error conditions, or performance characteristics. The description is minimal and lacks 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero wasted words. It's appropriately sized for a simple retrieval tool and front-loads the essential information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a retrieval tool with 2 parameters and 100% schema coverage but no annotations or output schema, the description provides basic purpose but lacks behavioral context and usage guidance. It's minimally adequate but has clear gaps in explaining how the tool behaves and when to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting, though no additional semantic context is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve') and target resource ('cards tagged as leeches from Anki'), making the purpose immediately understandable. It doesn't explicitly differentiate from its sibling tool 'tag_reviewed_cards', but the distinction is implied through the different operations (retrieval vs tagging).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 or in what context. The description only states what the tool does, without mentioning prerequisites, timing considerations, or relationship to the sibling tool beyond their different names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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