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Anki Connect MCP Server

Anki Connect MCP Server

Add notes (batch)

add_notes

Creates multiple Anki notes in one call and returns their IDs, using null for failed notes; validate inputs first to avoid duplicates or empty required fields.

Instructions

Creates several notes in one call. Returns an array of new note IDs in the same order as the input; an entry is null if that note could not be created (e.g. duplicate or empty first field). Run can_add_notes first to find out which notes will fail. If any attached file cannot be stored, no note is created and the error names the note (e.g. notes[2].picture[0]).

Input Schema

NameRequiredDescriptionDefault
notesYesNotes to create.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnly=false, destructive=false, idempotent=false), it discloses the failure model that matters most: partial success with null entries positionally aligned to the input, and the reasons a note can fail (duplicate, empty first field). It also states atomicity per note — if an attached file cannot be stored, that note is not created — and gives the error addressing format (notes[2].picture[0]).

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?

Three sentences, front-loaded with purpose, then return shape, then the pre-validation pointer and the atomicity caveat. Every sentence carries distinct information with no repetition of the title or schema.

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?

There is no output schema, so the description must explain return values — and it does precisely (ordered array of IDs, null for failures). With batch atomicity, error addressing, and the pre-check tool all covered, an agent has everything needed to call this correctly.

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 100%, so the baseline is 3, but the description adds real meaning about how the single `notes` parameter behaves: results are order-aligned, and a null entry signals rejection of that element rather than a call failure. That is semantics the schema cannot express.

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 opening sentence gives a specific verb and resource plus the defining scope: "Creates several notes in one call." The batch framing distinguishes it from the singular add_note sibling without the agent needing to open a schema.

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?

It gives an explicit precondition — "Run can_add_notes first to find out which notes will fail" — naming a concrete sibling to call beforehand. What it leaves implicit is the routing decision for the single-note case (add_note) and whether validation is mandatory or merely recommended.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.