notice_windowsEvery sub-processor notice window Xither can quote: the number of days (business days where the clause says so) each vendor's own terms give customers to object to a new sub-processor, with the verbatim clause, its source URL and the date Xither read it. The same table as www.xither.com/notice-windows. A vendor not listed has no window Xither could quote.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is low. The description adds genuinely useful context beyond them: it guarantees completeness ('Every... Xither can quote'), explains the business-days nuance, and discloses provenance metadata (verbatim clause, source URL, date read), which tells the agent the data is auditable and current-as-of.
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?
Three sentences, front-loaded with what the tool returns, then an external reference, then the negative case. Each sentence earns its place, though the phrasing is slightly dense and the web-table reference is mildly redundant.
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 no output schema and no parameters, the description carries the full burden of explaining return values, and it does so completely: field list, business-day caveat, provenance, and the semantics of a missing vendor. Nothing needed to interpret the response is absent.
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?
The tool takes zero parameters, so the baseline of 4 applies. There is nothing for the description to disambiguate, and it correctly spends its words on the return shape instead.
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 names a specific resource (sub-processor notice windows) and enumerates exactly what each row contains: days count, business-day caveat, verbatim clause, source URL, and read date. It is clearly distinct from the vendor-lookup siblings, though it does not explicitly name them to route the agent.
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?
Usage is implied by the dataset description rather than stated: there is no explicit 'use this when...' or routing to lookup_vendor/search_vendors for related needs. The negative-case rule ('A vendor not listed has no window Xither could quote') is useful interpretation guidance, which lifts it above a bare 2.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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