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List automations

list_automations
Read-onlyIdempotent

AI Agents (inside one system): Use when the customer asks which automations run in their system, whether a rule ran, or to find one to pause, resume, change or remove. Returns each rule as a sentence (when, only if, then) with its id, status (proposed, active or paused), last run, runs in the last 30 days and why it paused, plus the approval link of a rule that still waits for the customer. Leave the system out for every system. Not for the changes the assistant made (use list_changes).

Input Schema

NameRequiredDescriptionDefault
systemNoOptional: which AI agent, by the system's id from list_ai_systems or its name. Leave it out for all of them.

Output Schema

NameRequiredDescriptionDefault
noticeNo
systemIdYes
maxActiveYes
systemNameYes
automationsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds useful non-obvious context: the status vocabulary (proposed/active/paused), the 30-day run-count window, why-paused reason, and the approval link for rules awaiting customer action. Return-shape detail is partly redundant with the output schema, keeping it just short of a 5.

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-loads the trigger condition and packs the alternative-tool exclusion at the end, with no filler sentences. The middle sentence is a long run-on enumeration of return fields that could be tightened, but every element is informative.

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?

One optional parameter fully documented in the schema, an output schema covering return values, and annotations covering safety — the description fills the remaining gaps (trigger intents, sibling exclusion, status vocabulary). An agent has everything needed to call this correctly.

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% and the single parameter is optional, so the schema already carries the semantics. The description's 'Leave the system out for every system' restates what the schema says ('Leave it out for all of them'), adding no new syntax or format information.

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?

States a specific verb+resource (list automations) and enumerates the concrete customer intents it serves: which automations run, whether a rule ran, or finding one to pause/resume/change/remove. It explicitly distinguishes itself from the closest sibling ('Not for the changes the assistant made (use list_changes)').

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 explicit trigger conditions ('Use when the customer asks...'), a scoping rule ('Leave the system out for every system'), and names the alternative tool plus the condition that selects it (list_changes for assistant-made changes). Nothing about when to pick this tool is left to inference.

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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