Translate a word or phrase. Single words use 47-language dictionary (instant). Sentences use AI translation.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Word or text to translate | |
| target_lang | No | ISO language code (es, fr, de, zh, ja, etc.) | es |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It adds meaningful behavioral traits by revealing that single words leverage a 47-language dictionary (instant) while sentences use AI translation, which is not obvious from the schema. It does not mention rate limits or permissions, but for a non-destructive translation tool, the disclosed behavior is sufficient to score above baseline.
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 short sentences with no fluff. The key verb and object are front-loaded in the first sentence, and the second provides necessary behavioral detail. Every word earns its place.
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 tool is simple with two parameters and no output schema. The description clearly states what it does and the two translation modes. Although it does not explicitly describe the return format, that is inferable for a translation tool. The schema covers parameters fully, and the description adds enough behavioral nuance to be complete for the given complexity.
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 description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining that the 'input' parameter's behavior depends on whether it is a single word or a sentence, affecting the translation engine used. This enriches the semantics of the input parameter beyond the schema's simple 'Word or text to translate'.
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 starts with a specific verb and resource: 'Translate a word or phrase.' It clearly distinguishes this from sibling tools like lookup_word or define_term by focusing on translation. It also adds behavioral nuance by splitting single words vs sentences, enhancing purpose clarity.
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?
The description provides clear context on when to use this tool: for any word or phrase translation. It adds usage guidance by explaining that single words use the dictionary and sentences use AI, which helps set expectations. However, it does not explicitly name alternative tools or state when not to use this tool, so it misses full exclusion 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 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.