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Rewrite a custom monitor's recipe

reauthor_custom_check
Destructive

Have the AI write a custom monitor's recipe again: answer its question (status needs-input) with answer, or retry after it failed or went stale. The monitor stops running until the new recipe is approved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerNoYour reply to the AI's question (get_check custom.question)
checkIdYesThe custom monitor's id

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, openWorldHint=true and idempotentHint=false, so the safety profile is covered. The description adds a genuinely useful behavioral fact beyond the annotations: the monitor stops running until the new recipe is approved. It does not disclose cost, auth, or what happens to the previous recipe.

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?

Two tight sentences with no waste: the action is front-loaded, followed by the conditional branch and the key consequence. Every clause earns its place.

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 mutation tool with a 2-param schema, rich annotations, and no output schema, the description covers the trigger conditions and the operational consequence (monitor halts pending approval). It omits what happens to the prior recipe and any approval-dependency link to approve_custom_check, but nothing critical to calling it correctly is missing.

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?

Schema description coverage is 100%, so the schema already documents both `answer` and `checkId`, and the description does not add syntax or format detail. It reinforces that `answer` is the reply to the AI's question, but this is already in the schema. Baseline 3 is appropriate when the schema carries the parameter meaning.

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

Purpose4/5

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

The description states a specific action on a specific resource ('Have the AI write a custom monitor's recipe again') and ties it to the reauthor concept, which is distinguishable from add_custom_check and edit_custom_check. It stops short of naming an explicit sibling to distinguish from, but the 'again' framing plus the approval note makes the intent clear.

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?

It gives concrete trigger conditions: use `answer` when the monitor status is needs-input, or retry after a failure or staleness. That is clear when-to-use guidance. It does not explicitly name alternatives (e.g., approve_custom_check or edit_custom_check) or state when NOT to use it.

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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