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sameerbajaj

AnkiWeb MCP

by sameerbajaj
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learnings_confirm

Confirms an open candidate by printing its full payload, then materializes it to apply pending AnkiWeb changes.

Instructions

Confirm an open candidate: print its full payload, then materialize it

Input Schema

NameRequiredDescriptionDefault
idYesPositional argument <id>
csvNoOutput as CSV (table and array responses) (default: false)
yesNoSkip confirmation prompts (explicit confirmation for scripts) (default: false)
jsonNoOutput as JSON (default: false)
agentNoSet agent-friendly output defaults (--json --compact --no-input --no-color) (default: false)
plainNoOutput as plain tab-separated text (default: false)
quietNoBare output, one value per line (default: false)
selectNoComma-separated fields to include in output (e.g. --select id,notetype_id,deck_id)
compactNoReturn only key fields (id, name, status, timestamps) for minimal token usage (default: false)
dry-runNoShow request without sending (default: false)
max-ageNoMaximum acceptable age of local-store data before a stderr hint suggests sync; 0 disables (default: 30m0s)
receiptNoWrite an atomic private run receipt (default: false)
timeoutNoRequest timeout (default: 1m0s)
no-cacheNoBypass response cache (default: false)
no-colorNoDisable colored output (default: false)
no-inputNoDisable all interactive prompts (for CI/agents) (default: false)
no-learnNoDisable the teach/recall learning loop for this invocation (default: false)
idempotentNoTreat already-existing create results as a successful no-op (default: false)
rate-limitNoMax requests per second (0 to disable; default auto — pace to server rate-limit headers) (default: auto)
data-sourceNoData source for read commands: auto (live with local fallback), live (API only), local (synced data only) (default: auto)
client-profileNoSelect the tenant-gated client profile (env: ANKIWEB_CLIENT_PROFILE)
human-friendlyNoEnable colored output and rich formatting (default: false)
allow-partial-failureNoDowngrade response-body partial-failure (e.g. partialFailureError) to a warning instead of a non-zero exit (default: false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

The annotations already establish that this is a non-read-only mutation that is not idempotent. The description adds useful behavior beyond annotations by saying it first prints the full payload and then materializes the candidate, but it does not explain what materialization does, whether it is reversible, or how confirmation behaves.

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 front-loaded sentence with no filler. It is appropriately brief, though its terseness contributes to gaps better handled in other dimensions.

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?

For a mutation tool with 23 parameters and no output schema, the description is too thin. It omits what materialization means, how the confirmation flow works, and how this tool relates to sibling learnings commands, leaving the agent to infer too much.

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 23 parameters, including id, output formats, confirmation, and rate-limit controls. The description adds no parameter-specific meaning beyond the schema, so the baseline score of 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?

The description uses a specific verb ("Confirm") and resource ("open candidate") and adds the two-step behavior of printing the payload before materializing it. It does not differentiate this tool from sibling alternatives such as learnings_reject or learnings_candidates, and "materialize" remains somewhat opaque.

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?

It implies the tool applies to an open candidate, but gives no explicit guidance on when to choose confirm versus reject or other learnings tools. There are no exclusions, prerequisites, or alternative-routing instructions.

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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