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skylight_get_auto_creation_intent

Get the current status and draft results of an AI auto-creation request to confirm whether a calendar item, list, or task was generated as intended.

Instructions

Get an AI auto-creation intent (its status + draft results).

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

B3.3/5.0
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 disclosing behavior. 'Get' implies a read-only operation, and the parenthetical '(its status + draft results)' explains what the tool returns. However, it does not disclose output structure, error behavior, or any side effects, though for a simple read tool this is acceptable.

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?

The description is a single, front-loaded sentence with no fluff. The parenthetical adds useful detail about return contents, and every word earns its place.

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?

For a tool with no output schema, no annotations, and two undocumented parameters, the description is too thin. It summarizes the return value but omits parameter semantics and usage guidance, leaving an agent to infer too much from sibling names and the bare schema.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain either parameter. The name 'id' is partially clarified by the tool purpose as the intent id, but 'frameId' is left completely unexplained. The description fails to compensate for the schema's lack of descriptions.

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 states a specific verb ('Get') and a specific resource ('an AI auto-creation intent'), and further clarifies that it returns both status and draft results. This distinguishes it from sibling tools like list_auto_creation_intents, approve_auto_creation, and undo_auto_creation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no explicit guidance about when to use this tool versus alternatives. An agent can infer that 'get' means retrieving a single intent by id, but the description does not state conditions, prerequisites, or mention sibling tools such as list_auto_creation_intents.

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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