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get_examples

Retrieve example notes from Anki to guide flashcard creation. Filter by deck, set limit, and choose sampling method: random, recent, most reviewed, best performed, mature, or young.

Instructions

Get example notes from Anki to guide your flashcard making. Limit the number of examples returned and provide a sampling technique:

    - random: Randomly sample notes
    - recent: Notes added in the last week
    - most_reviewed: Notes with more than 10 reviews
    - best_performance: Notes with less than 3 lapses
    - mature: Notes with interval greater than 21 days
    - young: Notes with interval less than 7 days

Args:
    deck: Optional[str] - Filter by specific deck (use exact name).
    limit: int - Maximum number of examples to return (default 5).
    sample: str - Sampling technique (random, recent, most_reviewed, best_performance, mature, young).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deckNo
limitNo
sampleNoSampling technique: random, recent (added last 7d), most_reviewed (>10 reps), best_performance (<3 lapses), mature (ivl>=21d), young (ivl<=7d)random

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully bears the burden of behavioral disclosure. It describes the retrieval of examples but does not specify any side effects, authentication requirements, or rate limits. It implies a read-only operation but lacks explicit safety guarantees.

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 bullet points for sampling techniques and a clear args list. It front-loads the purpose and provides necessary detail without excessive verbosity, though it could be slightly more concise.

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 three parameters and the presence of an output schema, the description adequately covers parameters and intended use. It does not explain the return format, but the output schema likely handles that. Overall, it provides sufficient context 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 low (33%), but the description adds value by explaining the 'sampling' parameter with detailed options and clarifying that 'deck' requires exact name and 'limit' defaults to 5. This compensates for the schema's lack of description on these parameters.

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 verb ('Get'), resource ('example notes from Anki'), and purpose ('to guide your flashcard making'). It distinguishes from siblings like 'search_notes' by focusing on sampling techniques for flashcard creation.

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 explains the use case (flashcard making) and details the sampling techniques, guiding when to use each option. However, it does not explicitly mention when not to use this tool or suggest alternatives like 'search_notes' for broader queries.

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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