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

With no annotations, the description carries the full behavioral burden. It adds useful context by specifying UTC time and per-brand breakdown, but it does not disclose permissions, read-only nature, pagination, rate limits, or return format details.

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, dense sentence with no filler. It front-loads the metrics and ends with the per-brand breakdown scope, making efficient use of limited space.

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 four-parameter analytics tool with no annotations, no output schema, and no schema descriptions, the one-sentence description is insufficient. It omits critical details such as date range behavior, granularity semantics, and expected response structure.

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 coverage is 0% and the description does not explain the four parameters. It hints at date range and brand filtering through 'over time (UTC)' and 'per-brand breakdown', but does not address granularity or clarify formats and defaults.

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 analytics content: posts, deliveries, published, failed, spend, dynamic-pricing usage over time, and per-brand breakdown. It lacks an explicit verb and does not differentiate from close siblings like get_usage, but an agent can still infer the tool's purpose accurately.

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 no guidance on when to use this tool versus alternatives such as get_usage or list_brands. It only states what data is returned, leaving usage context entirely implicit.

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