Check text for PII, profanity, reading level (Flesch-Kincaid), brand voice violations, GDPR/COPPA markers.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Text to check | |
| policy | No | Optional: {brand_voice: {tone, banned_words}, max_grade_level} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It correctly implies a non-mutating 'check' operation, but it does not explain what the tool returns (flags, scores, a report) or any additional processing behavior. This is a moderate gap for a tool with no output schema.
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 a single, front-loaded sentence. It uses a clear verb, lists concrete check types, and contains no filler or redundancy. Every word adds value.
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 covers the tool's overall capability and highlights the main compliance categories, and the schema documents both parameters including the nested policy object. The main missing piece is a description of the return format, especially because there is no output schema, but the current level is adequate for tool selection and invocation.
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 already documents both 'input' and 'policy', so baseline is 3. The description adds meaningful context by naming Flesch-Kincaid reading level, brand voice violations, and GDPR/COPPA markers, which gives the agent a clearer understanding of what the policy object and output will address.
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 a specific verb ('Check') and defines the resource ('text') plus the exact scope: PII, profanity, reading level, brand voice violations, and GDPR/COPPA markers. This clearly distinguishes it from the sibling dictionary/lesson tools, which have completely different purposes.
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 implies when to use the tool by enumerating concrete compliance checks, but it does not explicitly state exclusion criteria or alternatives. However, since none of the sibling tools perform compliance checking, the usage context is clear enough without an explicit 'use when' statement.
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.