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

Annotations already mark it read-only and non-destructive, and the description adds substantial behavioral context beyond that: the recency-weighted, outlier-damped methodology, the audience-online blending for Instagram and TikTok Business, the basis values (`own_data_and_audience`, `audience`, `defaults`), the 15-post threshold, and the required analytics:read scope.

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 purpose and usage trigger are front-loaded, and nearly every clause carries useful information. It is dense and long, but the detail about fallbacks and basis values earns its place; only minor restructuring would improve readability.

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 steps in to explain the return shape (top 3 slots plus per-day breakdown) and the `basis` and `audience_online` fields. It also covers the sparse-data case and the permission requirement, leaving no major gap for a read-only recommendation 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?

Schema description coverage is 100%, so both parameters are already documented. The description still adds meaning by emphasizing that results are for one platform, that audience-online support varies by platform, and that bucketing uses the user's timezone.

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: it returns recommended posting times (day + hour) for one platform, computed from the workspace's posting history. It clearly distinguishes itself from analytics siblings by focusing on recommendations rather than raw metrics, and it names the exact output (top 3 slots plus per-day breakdown).

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?

It explicitly says to use this before scheduling when the user asks 'when should I post?' or has not specified a time. It also explains fallback behavior when history is sparse, including what to tell the user so defaults are not mistaken for personalized data.

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