Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, but the description adds valuable context: it names the underlying model (Qwen 1.5 14B), its optimization (Japanese/Chinese), and warns about potential inaccuracies for proper nouns. This goes beyond what annotations provide.

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?

Two well-structured sentences. The first states purpose and technology; the second (in both English and Japanese) provides usage guidance and a key warning. No extraneous information, front-loaded with the core action.

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?

Given the open-world hint and AI-based nature, the description covers all essential aspects: conversion type (kana), input constraints (Japanese text, 500 chars max via schema), model details, and a critical limitation (potential inaccuracies). The output schema exists but is not described; the open-world hint makes this acceptable.

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 coverage is 100% with clear descriptions for both parameters (text and output). The description adds minimal new meaning beyond the schema, mainly reiterating the conversion purpose. Baseline 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?

Clearly states the verb 'Convert', the resource 'kanji-containing Japanese text', and the output formats 'hiragana or katakana'. Mentions the LLM backend and a specific use case (readings for proper nouns, names, place names), distinguishing it from sibling tools like romanization.

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 advises using the tool to obtain readings for proper nouns, names, and place names, and warns that models frequently guess readings wrong. This provides clear when-to-use context and a caution, though it does not explicitly contrast with siblings like 'name.romanize'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.