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amanharshx

ultralytics-mcp

by amanharshx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

datasets_delete

Destructive

Delete a dataset by slug, owner/slug, or ul:// URI to move its images and annotations to trash. Models trained on it remain, and trashed items are restorable for a limited time.

Instructions

Delete a dataset by slug, owner/slug, or a ul://owner/datasets/slug URI. Deleting a dataset moves its images and annotations to trash with it; models trained on it are not deleted. Trashed items remain restorable for a bounded window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYesDataset ref by slug, owner/slug, or a ul://owner/datasets/slug URI.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.15
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed1 schema field changedv0.1.13
    • changedInput schema / properties / dataset / description
      Previous value: -"Dataset ref by slug, owner/slug, or ul:// URI."New value: +"Dataset ref by slug, owner/slug, or a ul://owner/datasets/slug URI."
  3. Changed1 schema field changedv0.1.12
    • changedInput schema / properties / dataset / description
      Previous value: -"Dataset ref by id, slug, username/slug, or ul:// URI."New value: +"Dataset ref by slug, owner/slug, or ul:// URI."
  4. Changed1 schema field changedv0.1.6
    • addedInput schema / properties / dataset / description
      Added value: +"Dataset ref by id, slug, username/slug, or ul:// URI."
  5. Addedv0.1.1

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false and destructiveHint=true, so the safety profile is covered; the description goes further and discloses cascade scope (images and annotations move to trash) and that models are unaffected. The only weakness is the vague 'bounded window' for restorability, which never states an actual duration.

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?

Three short sentences, front-loaded with the operation and accepted identifiers, followed by the two consequence facts. Nothing is padded and each sentence carries distinct information.

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

Completeness4/5

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

No output schema exists, so the description must carry the outcome story, and it does cover cascade and restorability. It omits permission requirements, behavior on invalid/unknown slugs, and the actual permanence period, which a destructive single-parameter tool would ideally state.

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 'dataset' parameter is fully documented in the schema, including all three accepted formats. The description merely repeats that same enumeration, adding no syntax, validation, or edge-case meaning beyond the schema.

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 (Delete) and resource (dataset), plus the three accepted reference formats, so an agent can immediately distinguish it from datasets_get, datasets_list, and datasets_create. The cascade sentence further pins down the exact operation.

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

Usage Guidelines3/5

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

Usage is implied by the destructive verb, but there is no explicit when-to-use/when-not-to-use guidance and no routing to alternatives such as projects_delete or models_delete. The note that models trained on the dataset are not deleted is a useful consequence hint but is not framed as selection 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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