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

Annotations only declare readOnlyHint=true and openWorldHint=false. The description adds substantial behavioral context beyond these: the countRead opt-in gate, independence from the read list, the refusal error collection_count_forbidden, the '3 spots left' use case, and the exact return shape { count }. This is exactly the kind of behavioral disclosure that helps an agent anticipate outcomes. No contradiction with annotations.

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 three sentences long and front-loaded with the core purpose. Each additional clause adds behavioral detail. The spec reference 'spec B4, issue #1056' is potentially unnecessary for an AI agent and adds noise, but it does not harm usability. Overall, it is dense yet efficient.

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?

With no output schema, the description explicitly explains the return value ({ count }) and the error condition (collection_count_forbidden). It covers scope (whole-scope, no filter/paging), permission nuances, and the difference from list read access. This is sufficient for an agent to know when and how to call the tool safely and correctly.

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 input schema has 100% description coverage for both parameters (app_id and collection). The tool description adds minimal parameter-specific meaning beyond the schema, such as 'v2 app's collection', but does not elaborate on accepted formats or edge cases. Baseline 3 is appropriate because the schema already carries the semantic load.

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 states a specific verb and resource: 'live row count of a v2 app's collection'. It also explicitly scopes the operation as 'whole-scope total with no filter and no paging', which distinguishes it from row-listing operations like list_rows. The spec reference (B4, #1056) adds precision.

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 provides clear context: it is a whole-scope total, has no filter or paging, and is gated by the countRead opt-in. This implies when to use it (for counts only) and when it might not work (if opt-in is absent). However, it does not explicitly name alternative tools or provide a direct 'when not to use' statement.

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