analyze_tokens

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

No annotations are provided, so the description must disclose behavior. It states counting tokens and estimating costs, which implies a read-only operation, but lacks details on output format, error handling, or side effects. The description is adequate but not rich in behavioral context.

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 is front-loaded with the core action and efficiently adds 'estimate API costs' without waste. 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?

Given the lack of output schema and annotations, the description is too brief. It does not explain return values, cost calculation basis, or behavior with invalid model names. More context is needed for a tool with these characteristics.

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 0%, so the description should compensate. It hints that 'model' selects a tokenizer (model-aware) and 'text' is input, but does not explain parameter values, defaults, or the meaning of 'multiple configured models'. This is insufficient for full understanding.

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 verb 'Count tokens' and the resource 'tokens', with additional context of 'model-aware tokenizers' and 'estimate API costs'. It distinguishes from sibling tools (distill, compare, stabilize) which focus on different operations.

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 usage for token counting and cost estimation but provides no explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, but the sibling tools list offers context of different functionalities.

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