tokenize

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

No annotations are provided, so the description must cover behavioral traits. It only states the return value (list of tokens and count) but does not disclose read-only nature, performance characteristics, or any potential side effects.

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 sentence that gets to the point efficiently. It is concise with no wasted words, though it could be more structured with separate lines for purpose and output.

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 simple tool with two parameters and no output schema, the description is minimal. It does not explain the model parameter options, usage scenarios, or any constraints. More detail would improve completeness.

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 covers 100% of parameters. The description does not add meaning beyond the schema; it mentions 'YandexGPT model' but the schema already specifies the model parameter with a default. No additional context is provided.

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 tokenizes text using YandexGPT and returns a list of tokens and their count. The verb 'tokenize' and resource 'text' are specific, and the tool is distinct from siblings like 'complete' or 'embed_text'.

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?

No guidance is provided on when to use this tool versus alternatives. The description mentions 'YandexGPT model' but does not explain why one would choose tokenize over other text-processing tools like 'complete' or 'classify'.

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