Retrieve complete details for a Kaggle dataset by providing its owner and slug. Use it to inspect dataset metadata, files, and properties before download or analysis.
Get full dataset info.
Args:
owner: Dataset owner username.
dataset_slug: Dataset slug name.
| Name | Required | Description | Default |
|---|---|---|---|
| owner | Yes | ||
| dataset_slug | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must convey behavioral traits. It indicates a read operation ('Get'), which is non-destructive, but does not detail potential permissions needed, rate limits, or whether the returned info is fully comprehensive. For a simple retrieval, it is adequate but lacks depth.
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 extremely concise: one sentence stating the core action followed by a compact argument list. Every sentence is necessary and front-loaded. There is no extraneous information, making it easy for an agent to parse quickly.
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?
Given that an output schema exists, the description does not need to detail return values. It covers both required parameters with meaningful explanations. However, it could mention that the dataset must exist or handle potential error scenarios. Overall, it is sufficient for a straightforward retrieval tool.
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?
The schema has 0% description coverage (only titles), so the description's argument documentation adds significant value. It explains that 'owner' is the dataset owner username and 'dataset_slug' is the dataset slug name, clarifying their semantic roles beyond the schema's generic titles. This helps the agent construct correct invocations.
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 clearly states 'Get full dataset info.' with a specific verb and resource. Among siblings like dataset_create, dataset_delete, and dataset_download, this tool is uniquely positioned as a retrieval operation. The word 'full' suggests comprehensive information, distinguishing it from potentially partial fetches like dataset_metadata.
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 guidance on when to use this tool vs. alternatives such as dataset_metadata or datasets_list. It does not specify prerequisites, when not to use it, or any context for selecting this over similar tools. The agent must infer usage solely from the tool name and basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.