define_term
Get an age-calibrated definition for any term. Known words use dictionary data; unknown terms use AI.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | Term to define | |
| age_group | No | Target age group | adult |
Orb Platform
TDQS
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: known terms use dictionary data, unknown terms use AI, and output is age-calibrated. It does not describe return format or potential unpredictability of AI, but for a definition tool this is sufficient transparency.
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?
Two concise sentences, front-loaded with the primary action and resource. Every word contributes meaning, and the fallback behavior is efficiently stated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple, with no output schema. The description covers its core functionality and the dual-mode behavior, making it complete enough for an agent to select and invoke it. It does not explain return structure, but that is not critical for this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with 'term' and 'age_group' already described. The description adds value by explaining 'age-calibrated', which clarifies how the age_group parameter affects the output beyond just 'Target age group'.
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 uses a specific verb 'Get' and resource 'definition for any term', with the key differentiator of 'age-calibrated'. It also clarifies the dual behavior for known vs. unknown terms, which distinguishes it from sibling tools like lookup_word.
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 clearly communicates when to use this tool: when an age-calibrated definition is needed. It does not explicitly exclude alternatives or name sibling tools, but the 'for any term' context and unique age-calibration make the intended usage clear.
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
B3.4/5.0
Disambiguation4/5
Most tools have clearly distinct purposes: dictionary lookups, reverse searches, translation, compliance checking, lesson generation, and knowledge graph exploration. The only ambiguity is generate_text, which overlaps with define_term and translate_word, but its role as a general text generation tool is clear from the description.
Naming Consistency3/5
Naming is inconsistent, mixing verb_noun patterns (e.g., check_compliance, define_term, search_words) with get_* patterns (e.g., get_lesson, get_quiz) and the non-verb word_of_the_day. However, all names are descriptive and readable.
Tool Count5/5
13 tools is well-scoped for a language platform covering dictionary, translation, lessons, quizzes, related words, images, and compliance. Each tool earns its place without redundancy or bloat.
Completeness4/5
The server provides comprehensive coverage for language-related workflows, including lookup, translation, lesson and quiz generation, related words, images, and compliance checks. Minor gaps like pronunciation audio or progress tracking exist, but core operations are well covered.