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ultralytics-mcp

by amanharshx
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TypeScript
Remote

datasets_create

Create a dataset in your Ultralytics workspace with configurable name, slug, task, classes, owner, and visibility; defaults to private unless public is set.

Instructions

Create a dataset in your Ultralytics workspace. Defaults to private visibility (the platform defaults to public when visibility is omitted).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesDisplay name.
taskYesDataset task. Server-validated; for example detect, segment, or classify.
ownerNoWorkspace owner; defaults to the account owner.
datasetYesURL slug for the new dataset (distinct from the display name given by name).
classNamesNoInitial class names for the dataset.
visibilityNoVisibility "private" (default) or "public".
descriptionNoDataset description.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.15
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed4 schema fields changedv0.1.13
    • addedInput schema / properties / classNames / description
      Added value: +"Initial class names for the dataset."
    • addedInput schema / properties / description / description
      Added value: +"Dataset description."
    • addedInput schema / properties / name / description
      Added value: +"Display name."
    • changedInput schema / properties / task / description
      Previous value: -"Dataset task such as detect, segment, semantic, pose, obb, or classify."New value: +"Dataset task. Server-validated; for example detect, segment, or classify."
  3. Changed5 schema fields changedv0.1.12
    • addedInput schema / properties / dataset
      Added value: +{
+  "description": "URL slug for the new dataset (distinct from the display name given by name).",
+  "type": "string"
+}
    • addedInput schema / properties / owner
      Added value: +{
+  "description": "Workspace owner; defaults to the account owner.",
+  "type": "string"
+}
    • removedInput schema / properties / slug
      Removed value: -{
-  "type": "string"
-}
    • addedInput schema / properties / visibility / description
      Added value: +"Visibility \"private\" (default) or \"public\"."
    • changedInput schema / required
      Previous value: -[
-  "name",
-  "task",
-  "slug"
-]New value: +[
+  "name",
+  "dataset",
+  "task"
+]
  4. Changed1 schema field changedv0.1.6
    • addedInput schema / properties / task / description
      Added value: +"Dataset task such as detect, segment, semantic, pose, obb, or classify."
  5. Addedv0.1.1

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the safety profile is partly covered. The description adds a genuinely non-obvious behavioral trait: the tool defaults to private visibility even though the underlying platform defaults to public when omitted, which is exactly the kind of gotcha that changes how an agent calls it.

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?

Two tightly written sentences with zero filler, and the important default-behavior caveat is front-loaded after the core purpose.

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?

For a mutation tool with no output schema, complete parameter docs, and annotations covering the safety profile, the description supplies the one piece of behavior an agent would otherwise get wrong (the visibility default). It stops short of noting idempotency or permissions, but the annotation already flags non-idempotency.

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%, so the schema already documents all seven parameters including visibility and the private/public values. The description's visibility-default note reinforces rather than extends the schema, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Create a dataset in your Ultralytics workspace'), which clearly distinguishes it from siblings like datasets_list, datasets_get, and datasets_delete. It does not explicitly name a sibling it should be chosen over, but the create semantics are unambiguous.

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?

There is no explicit when-to-use or when-not-to-use guidance and no alternatives are named. The create verb implies its purpose, but for a workspace that also has dataset_version_create and dataset_ingest, the description gives no help in choosing among them.

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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