Scatter a number of dates across a span, for generating events. The seed is required and the draw is fully deterministic: the same calendar, span, count, seed and unique flag always give the same dates, on any machine. unique=true (default) draws without repeats and fails if count exceeds the number of days in the span. Results come back sorted.
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | ||
| seed | Yes | ||
| count | Yes | ||
| start | Yes | ||
| unique | No | ||
| calendar | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so well: it discloses determinism across machines, the required seed, repeat/unique behavior, the failure condition when count exceeds available days, and that results are sorted. This is substantial behavioral context beyond the bare schema.
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 deliver the core purpose, determinism guarantee, uniqueness semantics, failure mode, and sort order. Every sentence earns its place, and the most important behavioral constraints are front-loaded.
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?
The tool has six parameters, three nested open objects, no output schema, and no annotations. The description explains determinism, uniqueness, failure, and sorted results, but it omits how 'calendar', 'start', and 'end' should be represented or constructed, and it does not describe the format of the returned dates beyond being sorted.
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 0%, so the description must compensate for undocumented parameters. It adds useful meaning for 'seed' (required, deterministic) and 'unique' (default true, no repeats, failure condition), but it does not clarify the structure or semantics of 'calendar', 'start', and 'end', which are open objects with no schema documentation.
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 opens with a specific verb ('scatter'), a clear resource ('a number of dates across a span'), and an intended use ('for generating events'). It is clear about what the tool does, though it does not explicitly name or contrast sibling tools, so it stops short of full sibling differentiation.
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 phrase 'for generating events' provides a clear contextual trigger for when to use this tool. It does not explicitly state when not to use it or point to alternatives, but the intended use case is unambiguous enough for selection among the listed date-related siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct calendar operation, and the descriptions carefully draw boundaries between related concepts. There is modest overlap: convert_date also reports a named period, and calendar_events can answer nth-weekday questions that overlap with nth_weekday_of_month, so an agent might occasionally pick the wrong entry point.
All names are readable and consistently snake_case, but the set mixes verb-led names like convert_date and render_timeline with noun-led names like calendar_events and moon_phases, plus one prepositional verb in add_to_date. This is a readable mixture rather than a uniform verb_noun pattern.
Fifteen tools sits at the upper end of the typical well-scoped range, but every tool fills a distinct role for an almanac-style server: date math, conversion, calendar queries, formatting, parsing, rendering, lunar and solar calculations, generation, and validation. The batched plural parameters also make the count feel intentional rather than bloated.
The surface covers the domain unusually well: conversion, arithmetic, events, periods, weekdays, format/parse, SVG rendering, moon phases, daylight, scattering, and validation all have dedicated tools. The main gap is introspection—there is no direct way to list a calendar spec's months, eras, week structure, periods, or formats beyond what other tools reveal through errors and outputs.