Anki Card Manager (acm)

by Eduvent

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but there is minor overlap between acm_annotate, acm_auto_classify, and acm_ingest regarding classification. Also, acm_audit and acm_reorganize both involve duplicate detection. Overall, an agent can generally distinguish them.
Naming Consistency4/5
All tools share the 'acm_' prefix and most use verb-based names (e.g., acm_annotate, acm_ingest). However, a few are nouns (acm_stats, acm_taxonomy), breaking the verb_noun pattern. Still, naming is largely predictable.
Tool Count5/5
13 tools is well-suited for an Anki card manager. Each tool addresses a specific stage in the workflow (annotate, classify, ingest, review, sync, etc.) without being excessive or insufficient.
Completeness4/5
The tool set covers the core lifecycle: candidate annotation, classification, ingestion, review, sync, and audit. Minor gaps exist, such as lacking a direct card edit tool (though acm_resolve provides correction). Overall, the surface is comprehensive for the domain.
Average 3.9/5 across 13 of 13 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 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
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Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
No annotations are provided, so the description must disclose behavioral traits. It mentions using a 'local duplicate engine' but does not indicate whether the tool is read-only, modifies data, requires permissions, or has rate limits. The search nature implies read-only, but this is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no extraneous information. It is front-loaded with the purpose. However, it could benefit from a brief structure like parameter hints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description is insufficient for an agent to understand the query mechanism, the meaning of similarity, or the role of optional parameters. The tool has 4 parameters and no parameter explanations, making it incomplete for complex usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning no parameter descriptions in the schema. The overall description does not explain any of the four parameters (front, back, deck, profile) or how they affect similarity search. This is a critical gap for an agent to use the tool correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool finds similar cards using a local duplicate engine. The verb 'busca' and resource 'tarjetas' are specific. However, it does not explicitly differentiate from sibling tools like acm_ingest or acm_review, but the function is distinct enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description only states the function; it does not provide context, preconditions, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden. It describes the multi-step process and idempotency/duplicate avoidance, but lacks details on error handling, permissions, or what happens when classification fails.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with Args and Returns sections. First sentence captures purpose. No redundant sentences, although length is moderate for a pipeline tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool complexity and presence of an output schema, the description covers the ingestion process, parameters, and return format adequately. Could mention potential errors or prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description adds significant value. It fully describes cards_json fields and on_exact_match options. The verbose parameter only gets a default mention, but its simplicity compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it processes candidate cards via normalization, classification, deduplication, decision, and persistence. It provides a specific verb+resource but does not explicitly differentiate from siblings like acm_auto_classify or acm_resolve.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It details the on_exact_match parameter behavior but does not specify use cases or exclusions for sibling tools.
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 the full burden. It mentions the return format but does not disclose if the operation is read-only, requires authentication, or has any side effects. For a read-only statistics tool, stating it is non-destructive would add value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences, no fluff, and the most important information (what it does and what it returns) is front-loaded. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there are no parameters and an output schema exists (context signals), the description adequately explains the return value. It could be more complete by describing the meaning of the actions, but for a simple stats tool it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the input schema is fully covered. The description adds value by specifying the output structure (JSON with counts by action and total), which is not detailed in the schema. According to calibration, 0 parameters baseline is 4, and the description meets this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool shows statistics of processed cards, using a specific verb ('Muestra') and resource ('registro de tarjetas procesadas'). It is clearly distinct from sibling tools like acm_annotate or acm_ingest, which perform different actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. The description only states what it does without any context on prerequisites, limitations, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 transparency. It discloses that dry_run previews without changes, actual run performs a backup, auto-tags high-confidence notes, reports duplicates for review, and never merges. This provides good insight into side effects and safety, though it could mention impact on existing tags.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat verbose with internal codes like E7-3 and E9-1 that may confuse an AI agent. It has a clear Args section but could be more concise. The use of bullet-like formatting helps, but overall it's not maximally efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 params, no annotations, but has output schema), the description covers the main behavioral aspects: preview, backup, autotagging, and duplicate reporting. It addresses all parameters and the tool's core action. It could mention return value specifics despite having output schema, but it's largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 explains all four parameters: deck (root deck), dry_run (preview vs apply), profile, and include_subdecks. The dry_run parameter is well-described with its default and effects. This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: massive reorganization of a deck, including retagging and reporting duplicates. It uses specific verb 'reorganize' and resource 'mazo' (deck). However, it does not explicitly differentiate from sibling tools like acm_apply_tags or acm_audit, which have related but distinct functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use dry_run=True (preview) vs False (actual application). It also notes that it never merges duplicates (decision #2). However, it lacks explicit guidance on when to choose this tool over alternatives like acm_annotate or acm_auto_classify, and doesn't mention prerequisites or exclusions.
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. It mentions that 'add' creates a 'category::value' tag, implying mutation, but does not disclose side effects, authorization requirements, rate limits, or what happens to existing tags. The description is minimal for a tool with both read and write operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a clear 'Args' section and a 'Returns' note. It front-loads the purpose and uses bullet-like formatting. However, it could be slightly more structured (e.g., using lists) and avoids redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 4 parameters and two actions, the description adequately explains the main functionality and return values. The presence of an output schema reduces the need to detail return formats. It misses error conditions or edge cases, but for a straightforward tool, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description fully compensates by explaining each parameter's role: action (show/add), category (required for add), value (creates category::value), and profile (which taxonomy to query/edit). It adds useful context beyond what the schema provides, such as the behavior of the 'value' parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Consulta o edita la taxonomía de tags', specifying both actions (show and add) and the resource (tags taxonomy). This distinguishes it from sibling tools like acm_apply_tags or acm_annotate, which handle different aspects of tagging.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides basic usage guidance by explaining the two actions and which parameters are required for 'add'. However, it does not explicitly state when to use this tool versus siblings (e.g., for managing taxonomy vs applying tags to cards), nor does it mention when not to use it or provide alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses key behavioral traits: actions include physical deletion (purge), and 'correct' causes re-ingestion with re-deduplication/re-classification. It also states it works with any ID completeness. However, side effects of other actions (e.g., approve) are not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the purpose (single action to resolve item). It structures arguments in a clear list. References to internal sections (§5, §6, E5-1/E5-3) add domain-specific conciseness but may be cryptic without context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters and no output schema visible but indicated as present, the description covers the tool's purpose, actions, parameter dependencies, and re-ingestion behavior. It lacks explanation of return values (but output schema exists) and error conditions, yet is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 explains all 7 parameters: record_id (ID or prefix), action (4 values with results), front/back (required for correct), tags (optional format), note_type/deck (optional correction). The description adds meaning beyond the schema, though note_type/deck reference an external section (§5) without full explanation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it resolves an item from the review queue with a single action, listing specific actions (approve, reject, purge, correct). It distinguishes itself from siblings like acm_review by noting it works in any state, not just the active queue. However, it does not explicitly differentiate from all sibling tools, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for resolving review queue items and explains behavior for each action (e.g., 'correct' re-enters pipeline). It mentions it works in any state but does not provide explicit guidance on when to use this tool over alternatives like acm_review or acm_annotate. No 'when not to use' or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions the output format and fields, but does not explicitly state whether the tool is read-only, any side effects, or limitations like pagination. The description is adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, efficiently conveying purpose and output. Every word adds value; no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (no parameters, list output) and the description covers what it does and what it returns. It could mention that it returns all pending duplicates without filters, but it is sufficiently complete for this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the description does not need to add parameter semantics. Per guidelines, 0 parameters gets a baseline of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists cards marked as 'possible_duplicate' pending review, specifying the return format and fields. This distinguishes it from sibling tools like acm_resolve or acm_find_similar_card.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 such as acm_find_similar_card or acm_resolve. The description does not include context on when not to use it or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even with no annotations, the description discloses key behaviors: idempotent re-runs, queuing on disconnection, backup before push, dry_run mode, and undo capability via batch_id. This adds significant context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear statement, bullet points for edge cases, and a returns section. It is concise without unnecessary words, though some points could be more tightly integrated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the presence of an output schema (though not provided), the description covers input parameters, behavioral details (idempotency, queuing), return fields (synced, errors, batch_id, backup), and interaction with acm_undo. It is sufficiently complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains both parameters: dry_run shows a plan without touching Anki, and export_tsv exports to TSV along with the push. This fully compensates for the missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool pushes pending approved cards to Anki, is idempotent, and handles routing. However, it does not explicitly distinguish from sibling tools like acm_ingest or acm_resolve, though the reference to acm_undo provides some context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage context such as idempotency, handling when Anki is closed, and options like dry_run and export_tsv. It lacks explicit guidance on when not to use this tool versus alternatives among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses that the untagged mode auto-tags high-confidence notes, implying a write operation. It also mentions pagination for untagged. However, it does not explicitly state if other modes are read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a brief overview, then a bulleted list of modes, followed by an Args list. It is concise yet informative, with every part contributing to understanding. The Spanish language is appropriate for the context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, 3 modes) and no annotations, the description covers modes and parameter constraints well. However, it lacks details on return values beyond pagination for untagged mode. The presence of an output schema partially compensates, but the description could better explain what each mode returns (e.g., clusters for duplicates).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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. The Args section adds meaning for each parameter, specifying which mode each applies to (e.g., days for recent, include_registry for duplicates, limit/offset for untagged). It explains defaults and usage, significantly adding value over the schema's bare titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it audits an Anki deck with three modes, explaining each mode's purpose. It distinguishes from sibling tools by naming the modes and referencing internal identifiers (RF-F1), but does not directly compare to alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Each mode has a usage context: duplicates for dedup cross-deck, recent for reviewing recently added cards, untagged for missing taxonomies. It provides guidance on when to use each mode but lacks explicit exclusions or comparisons to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral aspects. It describes inputs, processing, and returns but omits details on side effects (e.g., whether tags are appended or overwritten), permissions required, or idempotency. It gives basic behavioral info but lacks depth expected for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is fairly concise, with a clear opening sentence followed by structured Args and Returns sections. It is front-loaded with purpose and usage. Some minor redundancy exists (e.g., repeating 'tags' in multiple places), but overall it is well-organized and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main features but leaves gaps: it does not clarify if file and assignments_json can be used together, does not specify prerequisites (e.g., must cards be classified first?), and only briefly mentions errors. Given the tool's complexity (two parameter modes, two target types), more detail would improve completeness for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description compensates by detailing the assignments_json parameter structure (JSON array with tags and either note_id or card) and the file parameter (optional path to JSON/YAML). It adds meaningful semantics beyond the schema, though examples or constraints are missing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool applies tags to cards, distinguishes between new cards (by batch index) and existing Anki notes (by note_id), and specifies it should be used after acm_auto_classify for cards in needs_review. This provides a specific verb-resource pairing and differentiates from its sibling acm_auto_classify.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool ('Úsalo después de acm_auto_classify para las tarjetas en needs_review') and what it can do (apply tags to new or existing cards). However, it does not explicitly mention when not to use it or provide alternatives beyond the implicit sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Sin anotaciones, la descripción detalla el comportamiento: para lotes SYNC borra notas de Anki y revierte tarjetas; para INGEST borra tarjetas del registro sin tocar Anki. Esto proporciona transparencia sobre los efectos secundarios.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
La descripción es concisa: dos párrafos claros. El primer párrafo explica el propósito y el comportamiento, el segundo da contexto adicional. Sin palabras innecesarias.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
La descripción cubre los dos tipos de lotes y sus efectos, y menciona de dónde obtener el batch_id. Podría añadir detalles sobre errores o requisitos, pero es suficiente para un solo parámetro y con schema de salida presente.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
El único parámetro 'batch_id' está bien descrito: se indica que es el id devuelto por acm_sync/acm_ingest y listado por acm_stats. La cobertura del schema es 0%, pero la descripción compensa completamente.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
El verbo 'Deshace' indica claramente la acción de deshacer un lote, y 'un lote por id' especifica el recurso. La descripción diferencia entre lotes SYNC e INGEST, lo que la distingue de otras herramientas hermanas como acm_sync o acm_ingest.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
La descripción indica que los lotes disponibles se pueden ver con acm_stats, proporcionando contexto de uso. Aunque no menciona explícitamente cuándo no usar la herramienta, la información sobre los tipos de lotes guía adecuadamente al agente.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description fully carries the burden. It explicitly states the tool is non-persistent (read-only), details the return structure (es_duplicado, matches, mazo_sugerido, etc.), and explains verbosity behavior. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear first sentence, followed by behavioral and parameter details. While comprehensive, it could be slightly more concise (e.g., integrating the return details more tersely), but overall it is efficient and organic.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (context signal), the description need not explain return values, but it does so anyway. It covers all necessary aspects: purpose, behavior, parameters, and usage context. Sibling tools are listed, providing additional orientation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 thoroughly explains both parameters: cards_json as a JSON array with required and optional fields, and verbose as a boolean controlling match detail. This adds significant value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('anota') and resource ('tarjetas candidatas'), clearly distinguishing it from siblings by emphasizing it does not persist data (SIN subir ni persistir nada). This directly addresses the core workflow step.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description clearly situates the tool in the 'crear → revisar → subir' workflow and states it does not write to registry or Anki, implying when to use. However, no explicit when-not or alternative sibling comparisons are given, leaving room for minor ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description provides full behavioral context: deterministic, no LLM, returns two buckets with specific structure. Discloses that needs_review cards need agent intervention. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise but comprehensive: purpose first, then usage flow, then parameter details with bullet points. Every sentence adds value, no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter and existing output schema, the description fully explains the tool behavior, return structure, and usage. No gaps for an agent to misuse it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has only one string parameter with 0% schema coverage. Description provides complete structure: JSON array with fields front, back, source, suggested_tags, note_type - adding meaning far beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool classifies cards deterministically without LLM, returning resolved and ambiguous ones. Distinguishes from siblings like acm_apply_tags and acm_review.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to call this first due to token efficiency, and that only cards in 'needs_review' require agent analysis and calling acm_apply_tags. Provides clear when-to-use and alternative.
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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