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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: it is a pure computation, it reflects the 2026 dataset, it does not infer closures from legal anchors, and it documents a REST fallback and docs URL. Minor deduction because it doesn't discuss rate limits or result determinism across dataset versions.

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 definition is front-loaded with the use case and scope, which is good, but the 'Example user requests' line and the verbatim formula add filler that doesn't change agent behavior. Still, most sentences earn their place and the structure is navigable.

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 an output schema present, the description needn't enumerate return fields, yet it does list them, and it supplies scope boundaries, dataset versioning, and a REST fallback. For a single tool in a very large sibling set it is nearly complete; it could more explicitly name the check_gh_working_day/add_gh_working_days alternatives to fully route the agent.

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 100%, so the schema already documents all four parameters including defaults for include_start_date/include_end_date. The description lists the inputs and repeats a complete JSON example, but that example is already present in the schema's 'examples', so it adds little beyond what structured data provides. Baseline 3 applies.

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+resource ('Counts authority-scope working and non-working dates in a range') for Ghana, and explicitly delimits scope to the nationwide statutory/public-holiday layer, distinguishing it from private-employer, bank, exchange, local and sector calendars. An agent can tell this apart from the many sibling count_*/check_*/add_* tools.

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 gives explicit when-to-use ('Use this when you need an auditable Ghana working-day count... return its calculation instead of answering from memory') and when-not ('Do not use this when this package covers the nationwide statutory/public-holiday layer only'), plus caveats about lunar dates, later Executive Instruments, and non-inference of extra closure days.

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