ck_token_audit

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

No annotations are provided, so the description carries the full burden. It discloses the types of outputs (word counts, token estimates, etc.) and implies a read-only audit (no mention of modifications). However, it does not discuss permissions, side effects, or runtime behavior, leaving some gaps.

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, front-loaded sentence that efficiently conveys the tool's purpose and outputs. Every word contributes value, with no redundancy.

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?

Without an output schema, the description adequately explains return values (word counts, token estimates, duplicate detection, recommendations). It covers the main aspects but could elaborate on the format of duplicate detection or optimization recommendations. Still, it is sufficiently complete for a simple audit tool.

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?

Input schema coverage is 100% with both parameters described. The description adds context about the tool's purpose but does not provide additional parameter-level details beyond the schema. Baseline score of 3 is appropriate.

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 audits project rule files and skills for token overhead, listing specific outputs (word counts, token estimates, duplicate detection, recommendations). This distinguishes it from sibling tools like ck_cost_optimizer, which focus on monetary cost rather than token overhead.

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 when to use the tool (to audit token overhead) but does not provide explicit guidance on when not to use it or specify alternatives among siblings. The context is clear but lacks exclusion criteria.

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