count_tokens

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

The description discloses the exact counts returned (tokens, characters, no-space characters, Arabic characters) and clarifies the definition of tokens. Since no annotations are provided, the description takes on full transparency burden and does so adequately, though it could explicitly state the tool has no 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 very concise, front-loading the purpose, then clarifying key terms, and listing parameters and returns in a structured way. Every sentence serves a purpose with no fluff.

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?

For a simple counting tool with one parameter and an output schema, the description adequately explains the return values (dict with specific fields). It could include an example or edge-case handling, but overall it is sufficiently complete.

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?

The only parameter 'text' receives a minimal description in the Args section ('The text to measure'), adding little beyond the schema title. With 0% schema description coverage, the description should provide more detail (e.g., encoding, length limits), but it does not.

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 counts basic statistics (whitespace tokens, characters, Arabic characters). It distinguishes itself from sibling tools like detect_dialect or normalise_arabic, which are about processing, not counting.

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 clarifies that 'tokens' means whitespace-delimited words, helping avoid misuse. However, it provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.

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