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Mark word struggled

mark_struggled

Spaced-repetition degrade for a word the user just got wrong: the card comes back sooner. ease_factor drops, interval resets, repetitions reset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage tag of the word: base ISO with an optional region (en, en-us, pt-br). Scoped on the base, so the variant the session instructions ask you to send always matches. Omit it and the user's own primary learning language is used.
wordYes
sourceNo
contextNo

TDQS

A4.2/5.0
Behavior5/5

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

The description transparently discloses the effects of the operation: card comes back sooner, ease factor drops, interval resets, repetitions reset. This goes beyond the annotations (readOnlyHint=false, destructiveHint=false) by explaining exactly what changes occur, which is valuable for the agent to understand side effects.

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 concise and to the point, using two sentences to convey the action and its results. There is no unnecessary verbosity or repetition, making it easy for an agent to parse quickly.

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?

The description explains the purpose and behavioral effects clearly, but it lacks context about what parameters are required (e.g., what 'source' and 'context' are for) and does not mention any return value or success criteria. While there is no output schema, the parameter ambiguity and lack of directions on when to use versus not use the tool make it only moderately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has four parameters (lang, word, source, context), but only 'lang' has a description, giving 25% coverage. The tool description does not compensate by explaining the meaning or usage of 'source' and 'context', leaving ambiguity. Since coverage is low, the description should have added parameter context, but it does not.

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 description clearly states the tool's purpose: to degrade spaced repetition for a word the user got wrong, with explicit effects (card comes back sooner, ease factor drops, etc.). It distinguishes from siblings like 'mark_demonstrated' by focusing on the 'struggled' state.

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?

The description implies when to use it (when the user got a word wrong) and explains the consequential behavior. However, it does not explicitly contrast with alternatives like 'mark_demonstrated' or provide explicit direction on when not to use it, though the context strongly suggests the intended scenario.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools cleanly separate single-card lookup, batch lookup, due queue, event log, and SRS adjustments. The main overlap is `get_system_instructions` and `get_user_profile`, which both return CEFR level, languages, due count, and weak words, so an agent could mis-select between them.

Naming Consistency5/5

Every tool follows a consistent lowercase snake_case verb_noun pattern: capture_*, get_*, mark_*, log_*, check_. The verbs are descriptive and predictable, making the set easy to navigate.

Tool Count5/5

13 tools is well within the ideal scope for a language-learning memory/assistant server. Each tool covers a distinct part of the capture, lookup, review, and spaced-repetition workflow without feeling padded.

Completeness4/5

The core lifecycle is well covered: grammar and vocabulary capture, batch deck checks, card detail, due queue, recent activity, and SRS boosts/penalties. Minor gaps exist: captured grammar mistakes have no retrieval endpoint, and there is no delete/dismiss path for unwanted cards, but agents can work around these.