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skylight_list_auto_creation_items

Retrieve AI-created draft items such as meals, activities, and list entries to review pending content before finalizing.

Instructions

List the draft items an AI intent created (the general draft reader — meal sittings, activities, list items, etc., which the event-only draft list does not surface).

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

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. 'List' makes the read-only nature clear, and the description adds useful scope context (general vs event-only drafts). However, it does not disclose pagination, response format, or any requirements beyond the implied id relationship.

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?

A single sentence delivers the core purpose, examples, and a useful contrast with the sibling tool. There is no wasted wording or repetition of schema information.

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 tool has no annotations and no output schema, and the description fails to clarify the key parameters needed to call it correctly. An agent would need to inspect sibling tools or infer the id semantics from the tool name and context, so the description is not self-sufficient.

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%, yet the description does not explain what the required 'id' refers to (presumably an AI intent id) or what 'frameId' does. An agent has to guess at the meaning of both parameters from context alone, which is insufficient.

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 resource ('draft items an AI intent created'), gives concrete examples (meal sittings, activities, list items), and explicitly distinguishes itself from the event-only draft list. This makes the tool's purpose unmistakable and differentiates it from its siblings.

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 clearly conveys that this is the general draft reader and that the event-only draft list does not surface these items, so the agent knows when this tool is the right choice. However, it stops short of explicitly naming the alternative tool or stating a when-not-to-use condition.

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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