List datasets in your Ultralytics workspace, optionally filtered by workspace owner or username, to browse available data for projects, training, or predictions.
List datasets in your Ultralytics workspace.
| Name | Required | Description | Default |
|---|---|---|---|
| owner | No | Workspace owner; defaults to the account owner. Takes precedence over username when both are given. | |
| username | No | Compatibility alias for owner. |
Changes observed during successful MCP inspections.
v0.1.15Input schema / additionalPropertiesAdded value: +falsev0.1.13Input schema / properties / owner / descriptionAdded value: +"Workspace owner; defaults to the account owner. Takes precedence over username when both are given."Input schema / properties / username / descriptionAdded value: +"Compatibility alias for owner."v0.1.12Input schema / properties / ownerAdded value: +{
+ "type": "string"
+}v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds no further behavioral context—no pagination, return format, auth requirements, or rate limits—so it earns little credit beyond what structured fields provide.
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, front-loaded sentence with no wasted words. While it is perhaps too sparse for the tool's context, it is structurally clean and concise.
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 simple two-optional-parameter list tool with rich schema coverage and safety annotations, the description is minimally adequate. However, with no output schema present, it omits any indication of return shape, and it fails to differentiate from siblings, leaving material gaps.
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 coverage is 100%, so both parameters (owner and username) are fully documented in the schema. The description mentions the workspace scope but adds no syntax, precedence rules, or defaults beyond what the schema already states; baseline 3 is appropriate.
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 states a specific verb ('List') and resource ('datasets'), scoped to 'your Ultralytics workspace.' It is clear but does not differentiate from sibling tools like explore_datasets or datasets_get, leaving the agent to infer when this list operation is preferable.
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 provides no when-to-use guidance, prerequisites, or alternatives. It does not mention explore_datasets, datasets_get, or any condition that would select this tool over other dataset-related siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.