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

The description goes well beyond the annotations. It discloses that metrics accrue over time, that missing channels are recorded as ABSENT (never zero), that failed reads are recorded as 'could not tell', that X is skipped by default due to per-call billing, and that the skip is always reported. This is rich behavioral context that aligns with the readOnlyHint:false (it writes) and idempotentHint:true (skips already-taken readings). No contradictions.

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 substantial but every sentence carries a distinct piece of information: core action, accrual logic, absent/failure handling, X's cost, and the reporting of skips. It is not terse, but it is well-ordered (front-loads the main purpose) and avoids redundancy. A bit tighter prose would make it excellent, but it earns a 4.

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?

The description covers the key behavioral aspects: when to invoke, how accrual works, how missing/failed data is represented, and the cost implications of X. It does not mention the return value (e.g., whether the collected metrics are returned or just stored) or how the time-series can be retrieved afterward. Given no output schema, this is a minor gap, but the tool is otherwise well explained.

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 schema already covers both parameters well (100% coverage), so the baseline is 3. The description adds meaningful context beyond the schema: it explains why includeMetered bills credits, why the default is to skip X, and that the skip is reported even when omitted. This extra context about cost and reporting justifies a 4.

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 action ('Fetch fresh performance numbers'), the target ('this brand's recorded posts'), and the outcome ('store them as a time-series'). It further clarifies the accrual logic and how missing or failed reads are recorded, making it distinct from similar metric tools like x_post_metrics or post_performance, which focus on single posts or other scopes.

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 does not explicitly contrast with sibling tools such as x_post_metrics, post_performance, or x_post_insights. It explains when to invoke the tool (when readings are due) and how it behaves (skipping already-taken readings), but it doesn't provide criteria for choosing this tool over alternatives or mention that this is the canonical way to collect time-series metrics for recorded posts.

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