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anki-connect-mcp

anki-connect-mcp

by SolarianGG
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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scheduling.get

scheduling_get
Read-only

Retrieve a card's authoritative scheduling state by ID to verify due dates, intervals, and review status before making changes.

Instructions

Read authoritative current scheduling state.

Anki-Connect method scheduling.get. Scope: collection. Effect: read. Completion: completed. Undo: none.

Possible errors: collectionUnavailable, internalError, invalidParams, notFound, staleSession.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "cardId": "<cardId>"
    },
    "name": "scheduling_get"
  }
}

Input Schema

NameRequiredDescriptionDefault
cardIdYesCard ID (decimal string, e.g. "1700000000001").
sessionNoOptional. Collection session ID: the `session` field returned by `collection_get`. Usually omit it — the server then uses the session current at the time of the call. Pass it to make the call fail with `staleSession` instead of acting on a different profile/collection than the one your IDs, cursors and tokens came from.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds genuinely new context: collection scope, effect, completion semantics, 'Undo: none', and an enumerated error set including staleSession. It does not describe what the returned state contains, but the behavioral disclosure itself is strong.

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?

Front-loaded with the one-line purpose, then compact labeled metadata lines (scope, effect, completion, undo, errors) that scan quickly. The metadata block is slightly telegraphic but every line carries information; no filler prose.

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?

There is no output schema, yet the description never says what 'scheduling state' comprises (due, interval, ease, queue position, etc.), which is the main thing an agent needs before calling a read tool. Error and scope coverage are complete, so the gap is limited to return-value shape.

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%, and the session parameter's staleness semantics are already fully explained in the schema. The description adds no parameter-level meaning beyond what the structured fields provide, so the baseline 3 applies.

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?

"Read authoritative current scheduling state" gives a specific verb (read) and resource (scheduling state) and the 'authoritative' qualifier hints it is the source-of-truth counterpart to derived tools. It does not name which sibling (scheduling_getQueue, scheduling_getDue, scheduling_predict) covers which need, so an agent still has to infer the boundary.

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?

There is no explicit when-to-use or when-not-to-use guidance relative to the many scheduling siblings, and no statement of preconditions beyond the implicit read framing. The agent must infer that this is the raw state read rather than the queue, due-date, or prediction views.

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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