Skip to main content
Glama
amanharshx

ultralytics-mcp

by amanharshx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

dataset_version_restore

Destructive

Restore a dataset to a prior saved version using its ref and version number, replacing current images, labels, and splits with that snapshot.

Instructions

Restore a dataset to a previously saved version by slug, owner/slug, or a ul://owner/datasets/slug URI, and an integer version number. Versions are listed via datasets_get (the versions array, already returned unprojected). This REPLACES the dataset's current images, labels, and splits with that snapshot outright: anything done since that version, including un-versioned manual annotation work, is discarded. Ships ungated anyway, since it is the undo tool — an auto-annotate run snapshots a version before labelling, so restoring that version undoes the run exactly, and a mistaken restore is itself recoverable by restoring a later version. Restore also reassigns image IDs: a pre-restore image ID still resolves afterward but returns an empty label array rather than a 404, so callers must re-list images (for example with dataset_images_list) after a restore instead of reusing held IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYesDataset ref by slug, owner/slug, or a ul://owner/datasets/slug URI.
versionYesVersion number to restore, as listed in datasets_get's versions array.

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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."
  2. Addedv0.1.12

TDQS

A4.4/5.0
Behavior5/5

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

The description goes well beyond the destructiveHint annotation by detailing exactly what is replaced (images, labels, splits), what is discarded (un-versioned manual annotation work), and the side effect on image IDs (held IDs resolve to empty label arrays rather than 404s). This is exemplary transparency for a destructive tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but purposeful, with each sentence carrying important operational or recovery information. It could be slightly better structured with clearer separation between the core restore behavior and the post-restore re-listing guidance, but it remains efficient and front-loads the primary purpose.

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

Completeness5/5

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

For a destructive, non-idempotent tool with no output schema, the description covers the full operational picture: how to invoke it, what happens destructively, how to undo a mistake, and what callers must do afterward (re-list images). No critical behavioral gap remains for an agent to call it safely.

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 coverage is 100%, and the schema already explains both parameters: dataset reference formats and version number sourced from datasets_get. The description reinforces these details and adds that versions are 'already returned unprojected,' but it does not fundamentally change or extend parameter understanding 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?

The description opens with a specific verb ('Restore a dataset to a previously saved version') and clearly identifies the resource and the accepted reference formats (slug, owner/slug, or ul:// URI). It also ties the operation to version listing via datasets_get, making it easy to distinguish from related version and dataset tools.

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?

The description gives concrete usage context: it is positioned as the undo tool for auto-annotate runs, explains that restoring an auto-annotate snapshot reverts the run exactly, and notes that a mistaken restore can be recovered by restoring a later version. It does not enumerate explicit exclusions or when-not-to-use conditions, but the use case is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools