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ultralytics-mcp

by amanharshx
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TypeScript
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dataset_version_restore

Destructive

Restore a dataset to a saved version by version number to undo later changes, 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 changedv0.1.15
    • 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. Addedv0.1.12

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already flag destructiveHint=true and idempotentHint=false, but the description adds the specifics that matter: exactly what is replaced (images, labels, splits), that un-versioned manual work is discarded, that the operation is itself recoverable by restoring a later version, and the non-obvious post-restore ID behavior (stale IDs return an empty label array rather than 404).

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?

Front-loaded with purpose and ref formats, then destructiveness, recovery, and the ID quirk in dependency order. The 'ships ungated anyway' clause is slightly discursive for a tool description, but each remaining sentence carries operational information an agent needs before a destructive call.

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 two-parameter destructive tool with 100% schema coverage, no output schema, and clear annotations, nothing material is missing: the agent knows the trigger, the ref formats, the collateral damage, the recovery path, and the required follow-up call.

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 both parameters are already fully documented in the schema, including the accepted ref formats and the pointer to datasets_get's versions array. The description largely restates that schema text, adding little semantic detail beyond it, so the baseline 3 applies.

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 precise verb (restore) and resource (dataset version), enumerates the accepted ref formats, and distinguishes itself from siblings like dataset_version_create and datasets_get. An agent can identify it and its counterpart tools without opening any schema.

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

Usage Guidelines5/5

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

Routes the agent explicitly: versions come from datasets_get's versions array, and held image IDs must be refreshed via dataset_images_list after the call. It also names the canonical use case (undoing an auto-annotate run) and explains why the tool is ungated, which tells the agent when invoking it is appropriate.

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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