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create_note

Add a new note to an Anki deck with specified fields, model, and optional tags for spaced repetition learning.

Instructions

Create a new note in the specified deck with the given fields and tags.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deck_nameYesName of the Anki deck to add the note to
model_nameYesName of the note type/model to use for this note
fieldsYesDictionary mapping field names to their values (e.g., {'Front': 'Question', 'Back': 'Answer'})
tagsNoOptional list of tags to add to the note
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 of behavioral disclosure. It states the tool creates a note but doesn't cover critical aspects: whether this is a write operation (implied but not confirmed), error handling (e.g., if deck doesn't exist), permissions needed, or what happens on success (e.g., returns note ID). For a creation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('Create a new note') and includes essential details without waste. Every word earns its place, making it easy to parse quickly. No unnecessary elaboration or redundancy is present.

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?

Given the tool's complexity (creation operation with 4 parameters, no output schema, and no annotations), the description is incomplete. It lacks information on behavioral traits (e.g., side effects, error conditions), usage context compared to siblings, and output details. For a tool that creates data without structured output documentation, more guidance is needed to ensure proper agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly (e.g., deck_name, model_name, fields mapping, optional tags). The description adds minimal value beyond the schema by mentioning 'fields and tags' but doesn't provide additional context like format examples beyond what's in the schema. 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a new note') and specifies the target resource ('in the specified deck') along with key attributes ('with the given fields and tags'). It distinguishes itself from siblings like 'create_notes_bulk' (single vs. bulk) and 'update_note' (create vs. update), though it doesn't explicitly mention these distinctions. The purpose is specific and actionable.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., deck or model must exist), compare to 'create_notes_bulk' for multiple notes, or indicate when 'update_note' might be more appropriate. Usage is implied by the action but lacks explicit context or exclusions.

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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