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Delete shifts

rotacloud_delete_shifts
DestructiveIdempotent

WRITE — deletes shifts from the rota (assigned employees lose the shift). Delete 1-100 shifts by id; select them first with rotacloud_find_shifts. Without confirm it only previews: it reads each shift and deletes nothing. Show the preview to the user, then call again with confirm: true. Guards re-check each shift right before its delete and skip it if it no longer qualifies: require_open (skip assigned shifts), require_shorter_than_minutes (skip shifts whose elapsed duration is not strictly less). After each delete the shift is read again to verify it is gone. DELETE /shifts/{id}.

Input Schema

NameRequiredDescriptionDefault
confirmNotrue = delete now. Omit or false = preview only (nothing is deleted).
shift_idsYesIds of the shifts to delete (1-100).
require_openNotrue = delete a shift only if it is still open (unassigned) at delete time.
require_shorter_than_minutesNoDelete a shift only if its elapsed duration (end − start) is still strictly less than this many minutes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the destructiveHint/idempotentHint annotations, it discloses the two-phase preview-then-confirm behavior, per-shift guard re-checks with skip (not error) semantics, post-delete verification reads, and the 1-100 batch bound. This is exactly the extra context annotations cannot carry.

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

Conciseness4/5

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

Front-loaded with the WRITE/destructive warning and the confirm workflow, then guard details. Dense but nearly every clause carries operational meaning; the trailing 'DELETE /shifts/{id}' endpoint is the only line with marginal value.

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?

For a destructive batch tool with no output schema, it covers safety, sequencing, guards, and verification well. It stops short of describing what the preview response contains, which is the one thing an agent must relay to the user before confirming.

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 coverage is 100%, so baseline is 3; the description earns above that by clarifying that guards cause a silent skip rather than a failure and that confirm gates actual deletion. It does not add format detail for the numeric guard threshold beyond what the schema already states.

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?

Opens with a specific verb and resource ('deletes shifts from the rota') and immediately states the consequence ('assigned employees lose the shift'). The reference to rotacloud_find_shifts clearly separates this destructive tool from the sibling listing/finding tools.

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?

Gives an explicit workflow: select ids with rotacloud_find_shifts, call without confirm to preview, show the preview to the user, then re-call with confirm: true. The when-not (omit confirm = nothing deleted) is stated outright, leaving no inference needed.

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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