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skylight_list_auto_creation_drafts

List AI-drafted events for review before approval. Retrieve pending event drafts generated by an AI intent to verify and approve them.

Instructions

List the events an AI intent drafted (for review before approving). For meal/activity engines the drafts are items, not events — use skylight_list_auto_creation_items instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
frameIdNo

Schema Changelog

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

  1. Changed2 schema fields changedv0.7.1
    • removedInput schema / properties / id / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "number"
      -  }
      -]
    • addedInput schema / properties / id / type
      Added value: +[
      +  "string",
      +  "number"
      +]
  2. First observedv0.4.6

TDQS

A3.9/5.0
Behavior4/5

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

There are no annotations, so the description carries the behavioral burden. 'List' conveys a read-only operation, and the description usefully explains that drafts are events for some engines but items for meal/activity engines. It doesn't mention pagination or return details, but the core behavioral context is present.

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 concise sentences, front-loaded with the operation and resource, followed by a necessary sibling-tool redirection. No filler or redundant restatement.

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

Completeness2/5

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

The purpose and sibling distinction are clear, but the tool has no annotations, no output schema, and completely undocumented parameters. An agent cannot reliably construct a valid call because the required 'id' is undefined in both the schema and the description.

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

Parameters1/5

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

Schema description coverage is 0%, and the description gives no meaning for the required 'id' or optional 'frameId' parameters. The required 'id' is typed as both string and number, so an agent cannot determine whether to pass an intent ID, draft ID, or frame ID.

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 uses a specific verb ('List') and a specific resource ('events an AI intent drafted'), and clarifies the purpose is review before approving. It also explicitly distinguishes this tool from skylight_list_auto_creation_items, so an agent can tell them apart.

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?

It clearly states the intended workflow ('for review before approving') and gives an explicit alternative for meal/activity engines: 'use skylight_list_auto_creation_items instead.' This is direct when-to-use vs. alternative guidance.

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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