mark_word_difficult
Idempotent
Manually mark a word as still-learning for the signed-in user (resets mastery toward learning band). Requires sign-in.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| word | No | ||
| card_id | No |
Vocab Voyage
TDQS
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide idempotentHint=true and destructiveHint=false. The description adds valuable context about resetting mastery and requiring sign-in, which goes beyond the annotations and clarifies the mutation behavior.
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 sentence that efficiently conveys purpose, effect, and prerequisite. Every word adds value with no redundancy.
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?
While the main action is clear, there is no mention of return value, success/failure indicators, or prerequisites beyond sign-in. Given no output schema, the description could be more complete for a mutation tool.
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?
Schema description coverage is 0%, and the description does not explain the two parameters ('word' and 'card_id'). The agent is left guessing their roles and whether both are needed, which is a significant gap.
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 marks a word as 'still-learning' and resets mastery, which distinguishes it from 'mark_word_known'. The verb 'mark' and resource 'word' are specific and unambiguous.
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 indicates this is for manual marking and requires sign-in. It doesn't explicitly contrast with sibling tools like 'mark_word_known' or 'record_word_result', but the context is clear enough for an agent to infer appropriate use.
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
A3.7/5.0
Disambiguation4/5
Most tools have clear, distinct purposes with detailed descriptions. A few pairs like study_plan_preview vs get_study_plan_recommendation or get_definition vs explain_word_in_context have subtle overlaps, but descriptions effectively differentiate them.
Naming Consistency4/5
All tool names use lowercase snake_case with a consistent verb_noun pattern. Some names are longer but follow the same structure. No mixing of conventions, though the variety of verbs is high.
Tool Count3/5
31 tools is on the high side for a vocabulary server. The scope is broad (definition, quizzes, games, progress, parent/tutor features), but many tools are specific, making the set feel heavy. It earns its count but could be trimmed.
Completeness4/5
The tool surface covers most user needs: learning, testing, progress tracking, parental involvement, and support. Minor gaps like class management or deletion operations exist, but core vocabulary workflows are complete.