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

Annotations already mark it read-only and non-destructive, but the description adds valuable behavioral context beyond that: p99 is an observation rather than a maximum, latency data is nullable and trailing-30-day, heavy:true responses can be large, and the returned payload includes sample counts. This gives an agent accurate expectations about what the call returns and how to handle it.

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 dense but every sentence earns its place: the first states exactly what is returned, the second gives concrete usage guidance, and the third warns about heavy responses and points to run_api parameters. It is front-loaded and contains no filler or 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 tool with an output schema, the description need not re-explain return values; here it goes beyond that by covering pricing units, latency caveats, sample counts, large-response behavior, and next-step guidance for run_api. An agent has what it needs to decide when to call this tool and what to do with the result.

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 the schema already fully explains sku_id and context. The description reinforces that sku_id is a SKU slug and hints at heavy-response handling, but it does not add significant parameter-level meaning beyond the schema. Baseline 3 is appropriate here.

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 ('Get'), a precise resource ('full definition of one API by SKU'), and enumerates exactly what is included: normalized JSON schemas, lane-level pricing, and latency percentiles with sample counts. This clearly differentiates it from sibling tools like list_apis or search_apis, which would not return all these details.

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 gives a clear use case: inspect latency before choosing a client or MCP timeout, and plan around heavy:true entries by using fields/max_items/summary when calling run_api. It does not explicitly state when not to use this tool or how it compares to list_apis/search_apis, so it falls short of a 5 but is much stronger than a vague generic statement.

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