Explain what a word means inside a specific sentence — useful when a word has multiple meanings.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | ||
| sentence | Yes | The sentence containing the word |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds that it takes a word and sentence but does not specify the return format or other behavioral details like handling of non-existent words.
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?
Single sentence with no waste, front-loaded with the action and immediately clarifies the use case.
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?
No output schema or return value description. While the tool is simple, a brief note on the output (e.g., a string explanation) would improve completeness.
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?
The schema description covers 'sentence' but not 'word'. The tool description clarifies 'word' as the target word to explain, compensating for the missing schema description. Overall adds meaning beyond the schema.
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 explains a word's meaning within a sentence, with a specific use case for ambiguous words. It differentiates from sibling 'get_definition' which likely provides general definitions.
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?
Mentions 'useful when a word has multiple meanings' indicating context for use. Does not explicitly exclude alternatives but provides clear situational guidance.
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.