slimtoken.estimate_tokens

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

Discloses key behavior: bundled/offline tokenizer, exact for cl100k, approximate for non-cl100k models. Without annotations, this carries the transparency burden; it also describes return shape (total + breakdown).

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?

Three sentences, front-loaded with the main function. Each sentence adds value—tokenizer type, return output, model parameter caveat.

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?

Covers the core operation, offline behavior, approximation caveat, and return structure. Lacks elaboration on the `tools`/`system` parameters, but the 'request body' phrasing implies they're included.

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 only documents model and format (40% coverage), and the description adds meaning for `model` (informational, approximate for non-cl100k). But it does not clarify `messages`, `tools`, `system`, or `format` usage beyond the schema's minimal description.

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?

Clear verb-object structure: 'Count tokens in a request body'. The addition of 'real cl100k_base tokenizer' and 'total + per-message breakdown' gives specific scope and differentiates from sibling tools like optimize or prune.

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?

Implies usage for counting tokens, but doesn't explicitly state when to prefer this over sibling tools like inspect_budget or optimize_messages. No exclusions or alternative guidance provided.

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