Use this when the user asks what a specific word means, requests its definition, part of speech, synonyms/antonyms, or an example sentence. Returns curated dictionary data from the Vocab Voyage corpus. Do not use for sentence-level meaning disambiguation (call explain_word_in_context) or for daily word prompts (call get_word_of_the_day).
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | The word to define |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive. The description adds that it returns 'curated dictionary data from the Vocab Voyage corpus,' providing source context but no additional behavioral traits beyond what annotations imply.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, front-loaded with purpose, then usage, then exclusions. No redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (1 required param, no output schema, no nested objects), the description provides sufficient context: purpose, usage conditions, exclusions, and data source.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% with description 'The word to define.' The description does not add new semantics beyond the schema, meeting the baseline for full coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns definitions, part of speech, synonyms/antonyms, and example sentences for a specific word. It also distinguishes from siblings explain_word_in_context and get_word_of_the_day.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use: 'when the user asks what a specific word means, requests its definition...' and when not to use: 'Do not use for sentence-level meaning disambiguation (call explain_word_in_context) or for daily word prompts (call get_word_of_the_day).' Names alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.