tokenize

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions tokenization into readings and dictionary entries, which gives some output context, but lacks details on performance characteristics (e.g., speed, limitations), error handling, or what 'morphological analysis' entails beyond splitting. For a tool with no annotations, this is insufficient to fully understand its behavior.

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, efficient sentence in Japanese that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded with the core purpose, making it easy to understand quickly.

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 no annotations, no output schema, and 3 parameters with full schema coverage, the description is minimally adequate. It explains what the tool does (tokenization) but lacks details on output format (e.g., structure of readings/dictionary entries), error cases, or performance limits. For a tokenization tool with no output schema, more context on return values would be helpful.

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 schema already documents all parameters (text, scanLength, parser). The description doesn't add any parameter-specific information beyond what's in the schema. It implies the 'text' parameter is for Japanese text, but this is already covered in the schema description. Baseline 3 is appropriate when schema does the heavy lifting.

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 clearly states the tool's purpose: 'テキストを形態素解析し、読みと辞書エントリに分割する' (Tokenize text through morphological analysis, splitting into readings and dictionary entries). It specifies the verb (morphological analysis/tokenization) and resource (Japanese text), but doesn't differentiate from sibling tools like 'lookup' or 'kanji' which might have overlapping functionality with Japanese text processing.

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 no guidance on when to use this tool versus alternatives. There are sibling tools like 'lookup' and 'kanji' that might handle Japanese text differently, but no explicit comparison or context for choosing this tool is given. Usage is implied (for Japanese text tokenization) but not contrasted with other options.

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