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sameerbajaj

AnkiWeb MCP

by sameerbajaj
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recall

Read-only

Checks prior learnings for a query before running discovery to surface existing findings and prevent duplicate research.

Instructions

Check prior learnings for a query before running discovery (LLM-fired, pre-discovery)

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)
limitNoMaximum number of learnings to return (default: 10)
plainNoOutput as plain tab-separated text (default: false)
queryYesPositional argument <query>
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)
min-confidenceNoMinimum confidence to include in results (default: 1)
debug-mismatchesNoInclude cross-entity mismatches in the envelope under mismatches[] (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

B3.4/5.0
Behavior3/5

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

Annotations already mark this as a safe read-only, open-world, non-destructive operation, so the bar is lower. The description adds the pre-discovery timing context, but it does not disclose behavior around local vs live data, caching, or what 'learnings' actually contain—details that matter for a tool with 26 parameters.

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?

A single front-loaded sentence with zero waste. It states the action, resource, and timing context immediately.

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?

With 26 parameters, no output schema, and undefined terminology ('learnings'), this description is too sparse. It does not explain what a learning is, what the return shape looks like, or how results differ from learnings_list. The schema covers parameters, but the description leaves major contextual gaps for a complex tool.

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 every parameter is already documented in the schema. The description adds no parameter-specific semantics beyond the shared 'query' concept, so baseline 3 is appropriate.

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?

States a specific verb ('Check') and resource ('prior learnings') plus a clear purpose ('before running discovery'). However, it does not distinguish this tool from sibling tools like learnings_list, teach_lookup, or search, leaving ambiguity about which one an agent should choose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'before running discovery' implies when to use the tool, giving a pre-discovery context. But it names no alternatives, exclusions, or conditions relative to the many sibling learnings and search tools, so usage guidance is only implied.

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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