Almanack

format_calendar_date

Write a date out to a pattern. pattern is either a name from the calendar's own 'formats' (names win) or a pattern written out. Tokens: {day} {day-name} {month} {month-name} {year} {era} {era-abbr} {era-year} {intercalary} {period}, plus one named for each unit the spec's 'day' block declares ({bell}), with an optional zero-pad width on the numeric ones as {day:2}. Write a literal brace as {{ or }}. The output is exactly reversible by parse_calendar_date with the same pattern, so use these two as a pair. Pass 'dates' instead of 'date' to write many to the same pattern, with 'times' alongside when each carries its own time. Answers up to 1000 in one call, in the order given, as {count, ok, results}. Prefer this over one call per item: the spec travels once instead of once each.

Input Schema

TableJSON Schema

Name	Required	Default
`date`	No
`time`	No
`dates`	No
`times`	No
`pattern`	Yes
`calendar`	Yes
`on_error`	No	fail

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed9 schema fields changedSep 1, 2026

removedInput schema / properties / date / additionalProperties
```
Removed value: -true
```

addedInput schema / properties / date / anyOf

Added value: +[
+  {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]

addedInput schema / properties / date / default
```
Added value: +null
```
removedInput schema / properties / date / type
```
Removed value: -"object"
```

addedInput schema / properties / dates

Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "additionalProperties": true,
+        "type": "object"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Dates"
+}

addedInput schema / properties / on_error

Added value: +{
+  "default": "fail",
+  "title": "On Error",
+  "type": "string"
+}

addedInput schema / properties / time

Added value: +{
+  "anyOf": [
+    {
+      "additionalProperties": {
+        "type": "integer"
+      },
+      "type": "object"
+    },
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Time"
+}

addedInput schema / properties / times

Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "anyOf": [
+          {
+            "additionalProperties": {
+              "type": "integer"
+            },
+            "type": "object"
+          },
+          {
+            "type": "integer"
+          }
+        ]
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Times"
+}

changedInput schema / required

Previous value: -[
-  "calendar",
-  "date",
-  "pattern"
-]New value: +[
+  "calendar",
+  "pattern"
+]

First observedSep 1, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and is highly transparent: it discloses the pattern language, optional zero-pad widths, literal brace escaping, the exact reversibility property, the response contract {count, ok, results}, that results preserve input order, and a maximum call of 1000 items. This is far beyond what the schema or annotations offer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense yet efficiently organized: purpose first, then token grammar, then batch behavior and recommendation. Every sentence adds necessary information—examples, limits, response shape, and a pairing note—without filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema and no annotations, the description covers the core behavior, pattern syntax, batching, response shape, and the relationship to parse_calendar_date. Minor gaps remain: the shape of the 'calendar' object and the semantics of 'on_error' are not described, but they are partially inferrable from sibling tools and the parameter defaults.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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. It does so well for the most complex parameter, pattern, and also explains the date vs dates distinction and the times companion. However, it says nothing about the required 'calendar' parameter or the 'on_error' parameter, which remain undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Write a date out to a pattern.' It then details the pattern token grammar (day, month, year, era, intercalary, period, zero-padding), the plural 'dates' form, and the response shape, distinguishing it clearly from parse_calendar_date for the reverse operation. Even without referencing other siblings, the tool's purpose is unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells the agent to use parse_calendar_date with the same pattern because the output is exactly reversible, and strongly recommends the batch form ('Prefer this over one call per item') with a 1000-item cap. It does not contrast with other siblings like convert_date or add_to_date, but the pairing and batch guidance provide a clear usage context.

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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TDQS

A4.2/5.0

Disambiguation5/5

Each tool targets a distinct date/calendar operation: arithmetic, conversion, weekday lookup, period membership, moon phase, daylight, event queries, formatting/parsing, and two different rendering outputs. Even superficially similar tools (calendar_weekday vs nth_weekday_of_month, calendar_events vs calendar_periods) ask clearly different questions that the descriptions make unambiguous.

Naming Consistency4/5

Names are all lowercase snake_case and mostly follow a verb_first or noun_query pattern, but there is a mix: add_to_date, convert_date, and parse_calendar_date are imperative, while moon_phases, sun_daylight, and calendar_events are noun phrases describing what they return. The calendar_ prefix on three tools and consistent rendering parse/format pairs help readability, so this is a minor deviation, not chaos.

Tool Count5/5

15 tools sits at the upper bound of the ideal range for a focused domain toolkit. Every tool addresses a genuine calendar use case (define, convert, query, render, validate, generate), and none feels redundant or extraneous.

Completeness5/5

The set covers the full lifecycle of working with a custom calendar: validation before use, conversion between representations, date arithmetic and intervals, weekday and period queries, recurring events and daylight/moon data, formatting/parsing, and visual rendering. No obvious dead ends or missing operations come to mind for the stated domain.

Resources

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