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learnings_stats

Read-only

Report learn-loop effectiveness metrics from the local events table to assess retention, recall, and study performance.

Instructions

Report learn-loop effectiveness metrics from the local events table

Input Schema

NameRequiredDescriptionDefault
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

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the description does not need to restate safety basics. It adds the useful detail that the metrics come from the local events table, but does not explain freshness, aggregation behavior, or sync fallback despite the max-age parameter.

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 front-loaded sentence with no wasted words. It immediately tells the agent what the tool reports and where the data comes from.

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?

For a read-only stats tool with full schema coverage and annotations, the description is enough to invoke it, but there is no output schema and the description does not describe the returned metrics or report shape. It also does not clarify how it relates to sibling learnings tools.

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 fully documents all 22 optional parameters. The description adds no parameter-level meaning beyond that, which fits the baseline 3 when 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 states a clear verb, resource, and data source: report learn-loop effectiveness metrics from the local events table. It is more specific than a tautology, but it does not distinguish this stats tool from sibling learnings tools such as learnings_list or learnings_candidates.

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 provides no explicit guidance on when to use this tool versus alternatives like learnings_list, learnings_candidates, or sql. The local events table phrase implies a read-only reporting context, but there are no usage conditions or exclusions.

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