skylight_undo_auto_creation
Undo an AI auto-creation intent by discarding its draft using the creation ID.
Instructions
Undo/discard an AI auto-creation intent and its drafts.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| frameId | No |
Undo an AI auto-creation intent by discarding its draft using the creation ID.
Undo/discard an AI auto-creation intent and its drafts.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| frameId | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.7.1Input schema / properties / id / anyOfRemoved value: -[
- {
- "type": "string"
- },
- {
- "type": "number"
- }
-]Input schema / properties / id / typeAdded value: +[
+ "string",
+ "number"
+]v0.4.6Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the operation discards the intent and its drafts, which is meaningful behavioral information. However, it does not state whether the action is reversible, whether it requires any confirmation, or whether it affects related items beyond drafts, such as generated events or photos.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler. It is front-loaded with the action and object. It could add a bit more scoping detail without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is thin. It does not explain return behavior, side effects on related entities, prerequisites (e.g., intent must exist and be in draft state), or the relationship between the two parameters. The sibling set shows this is part of a larger auto-creation workflow, and the description leaves the workflow context underspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It does not explain what 'id' refers to (an auto-creation intent ID vs. a draft ID) or what 'frameId' is for. The agent must infer from the tool name and sibling tools, which is risky.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb-resource pair ('Undo/discard an AI auto-creation intent and its drafts') and identifies the exact object being acted on. It is distinguishable from the sibling auto-creation tools (approve, list, get), though the phrasing 'and its drafts' could imply broader side effects than just the intent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is used to discard an AI auto-creation intent before approval, but it does not explicitly state when to use it versus approving or listing intents. The sibling context provides enough signal, but the description itself gives no when-to-use or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/chrischall/skylight-mcp'
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