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booking-gap MCP Server

Profile where dead time occurs

booking_gap_profile
Read-onlyIdempotent

Pinpoint which weekdays and hours contain the most structural dead time, exposing the exact booking patterns that cause unsellable gaps.

Instructions

Show when structural dead time occurs, broken down by weekday and by hour of day.

The total dead-time figure from booking_gap_analyze says how much is lost. This says where to look. Dead time concentrated in one weekday or one part of the day usually points at a specific booking pattern rather than a general problem.

Gaps are distributed proportionally across the hours they span, so a 40-minute gap running 13:50-14:30 is attributed to both hours.

Args: same as booking_gap_analyze, plus

  • topCells (number, default 8): how many worst weekday x hour cells to list

Returns a text report with a weekday table, an hour-of-day bar chart, and the worst weekday x hour cells.

Use when: "which days are worst", "when does the fragmentation happen".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bookingsNoBookings as structured objects. Provide either this or bookingsCsv.
currencyNoCurrency symbol prefix, e.g. "¥".
topCellsNoHow many worst weekday x hour cells to list.
periodEndNoLast date to analyse (YYYY-MM-DD). Defaults to the latest booking.
resourcesNoResources to include even when they have no bookings in the period.
hourlyRateNoOptional hourly price, used to express dead time as an amount. No default.
bookingsCsvNoBookings as pasted CSV or TSV with a header row. Column names are matched in English and Japanese (start/end/resource/date/status, 開始時刻/終了時刻/部屋/日付/ステータス). A separate date column plus HH:mm times is supported.
periodStartNoFirst date to analyse (YYYY-MM-DD). Defaults to the earliest booking.
openingHoursYesOpening hours. Use "default" for the usual day and override individual weekdays as needed, e.g. {"default": [["09:00","22:00"]], "sun": [["10:00","18:00"]]}. Use "24:00" to close at midnight.
timezoneOffsetNoVenue timezone offset, used only for inputs that carry an explicit offset or "Z".+09:00
turnoverMinutesNoCleaning or changeover time required between two bookings.
minBookingMinutesNoShortest booking a customer is allowed to make.
slotGranularityMinutesNoAllowed booking start interval, measured from midnight (30 means :00 and :30).
Behavior5/5

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

Annotations already mark the tool read-only and non-destructive, and the description adds a non-obvious calculation rule: 'Gaps are distributed proportionally across the hours they span, so a 40-minute gap running 13:50-14:30 is attributed to both hours.' It also discloses the output structure, which is not present in the annotations.

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 front-loaded with the core purpose and each subsequent block earns its place: sibling comparison, interpretation guidance, calculation nuance, args note, return format, and use-when phrases. It is appropriately sized for a 13-parameter analytical tool with no wasted words.

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

Completeness5/5

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

With no output schema, the description adequately explains the return value ('a text report with a weekday table, an hour-of-day bar chart, and the worst weekday x hour cells'). It also orients the agent among four sibling tools and covers the key behavioral nuance, making it complete enough for correct selection and invocation.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% parameter coverage, so the schema carries most of the burden. The description adds a useful cross-reference ('Args: same as booking_gap_analyze, plus topCells') and restates the topCells meaning, but this mostly duplicates the schema's existing description of topCells.

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: 'Show when structural dead time occurs, broken down by weekday and by hour of day.' It clearly distinguishes this tool from booking_gap_analyze, which reports the total dead-time figure, by presenting this as the 'where to look' breakdown.

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

Usage Guidelines5/5

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

The description explicitly names the sibling tool for aggregate analysis and provides concrete use triggers: 'Use when: "which days are worst", "when does the fragmentation happen".' This gives the agent clear selection guidance among the sibling tools.

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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