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

Beyond the read-only and idempotent annotations, the description reveals a subtle metric behavior: clicks are lifetime while aggregate conversions/commission are window-only, hence conversion_rate is intentionally omitted in aggregate. This insight is exactly the kind of hidden behavior an agent needs to avoid drawing incorrect conclusions.

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?

All sentences contribute substantive value and the most important decision, per_piece vs aggregate, is front-loaded. The explanatory tail about conversion_rate is dense but necessary to prevent misuse; no filler or redundant repetition of schema fields.

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 read-only analytics tool with no output schema, the description does an excellent job of telling the agent what results to expect: the relevant metrics (clicks, conversions, revenue, conversion_rate) and how they are calculated in each mode. It is sufficient for a correct call and interpretation.

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?

Even though schema coverage is 100%, the description adds crucial semantic nuance: post_id can be omitted to trigger the aggregate path (despite being marked required in the schema), and the period parameter only scopes aggregate conversions/commission, not clicks or per-piece metrics. This meaning goes well beyond the schema's terse field names, although the required contradiction could confuse an agent.

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 uses a specific verb with a clear resource: "Get analytics for a specific post"—and explicitly distinguishes two modes: per_piece analytics and agent-wide aggregate when post_id is omitted. This productive contrast makes it immediately obvious what the tool does and how it differs from a typical post-specific endpoint.

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 clearly explains when to expect piece-level vs aggregate data and provides the exact trigger: omitting post_id. It also cautions about the misleading conversion_rate exclusion in aggregate mode, which tells the agent how to interpret results. It does not name alternate siblings, but it gives enough context to call this tool correctly.

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