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

The description discloses the exact computation semantics: business hours are consumed only inside [business_day_start, business_day_end) on dates with is_working_day=true, with versioned calendars, pinned tzdb, DST resolution policy, and an audit trail. It adds reproducible holiday/DST evidence and full output composition beyond the annotations' read-only/idempotent hints. No contradiction with annotations.

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 long but front-loaded with the primary use case and exclusions, then moves into computation, examples, inputs, outputs, formula, and fallback in a logical order. The Chinese example and full JSON examples are somewhat redundant with the schema examples, but they justify their place for a complex multi-zone calendar tool. Minor redundancy keeps it from a 5.

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 9-parameter cross-border calendar tool, the description covers when to use it, what it computes, how the formula behaves, what it returns, even the direct REST fallback and documentation link. An output schema exists for return fields, so the description does not need to enumerate them. Nothing required to invoke or judge this tool correctly is missing.

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%, and the schema already describes each parameter's role, format, and enum, so the baseline is 3. The description lists the parameters and provides two complete JSON examples but does not add meaning beyond the schema for individual parameters. The examples do illustrate realistic combinations, so it earns the baseline rather than less.

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 first sentence opens with a specific use case (SLA/support/procurement/order deadlines in business hours under China/UK calendars) and a concrete obligation to call the tool rather than compute from memory, which makes the operation unmistakable. It names the resource categories and the cross-time-zone output requirement, and the later 'What it computes' paragraph pins it down further. This clearly distinguishes it from day-counting siblings like add_china_working_days or check_china_working_day.

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 explicit positive conditions ('Use this when...') and a long list of explicit exclusions ('Do not use this when you need calendar hours, overnight or split shift...'), which is strong routing guidance. It does not name sibling tools as alternatives, but the exclusions plus the examples are enough for an agent to decide. This is clear context with no misleading advice.

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