Find an English word given a description of its meaning. Use when the user describes a concept but doesn't know the word. Returns words ranked by semantic similarity across 162,000 English words.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (1-25) | |
| query | Yes | Natural-language description of the concept (min 2 chars) |
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 adds useful behavioral context by stating results are 'ranked by semantic similarity across 162,000 English words,' giving insight into the ranking algorithm and corpus size. It does not mention any side effects, but as a read-only search tool, this is less critical.
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?
The description is two sentences, front-loaded with the primary purpose in the first sentence and usage guidance plus output characteristics in the second. Every sentence adds value with no redundancy.
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?
The description provides purpose, usage, and key output behavior, which is sufficient for a simple search tool with a fully documented schema. It lacks information about output format or error conditions, but these are not critical given the tool's simplicity and absence of an output schema.
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 input schema already covers both parameters with 100% coverage: query as a natural-language description and limit with default and range. The description does not add new parameter-specific details beyond what the schema states, so a baseline score of 3 is appropriate.
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 uses the specific verb 'Find' with the resource 'an English word' and specifies the input condition ('given a description of its meaning'), clearly distinguishing it from lookup tools like lookup_word or search_words.
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 states the use case: 'Use when the user describes a concept but doesn't know the word.' It does not name alternative tools but clearly implies when to prefer it over others.
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 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 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.
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.
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.