Datasets API

Kaggle-MCP provides tools to search, download, create, and manage Kaggle datasets directly through Claude.

Available Tools

datasets_list

List available Kaggle datasets with extensive filtering options.

datasets_list(search="", user="", license_name="all", file_type="all", tags="", sort_by="hotness", size="all", page=1)

Parameters:

search: Term(s) to search for
user: Display datasets by a specific user or organization
license_name: Display datasets with a specific license (all, cc, gpl, odb, other)
file_type: Display datasets of a specific file type (all, csv, sqlite, json, bigQuery)
tags: Tag IDs to filter by (comma-separated)
sort_by: Sort datasets by (hotness, votes, updated, active)
size: Filter by dataset size (all, small, medium, large)
page: Page number for results paging

Returns: JSON string with dataset details

dataset_list_files

List files in a specific dataset.

dataset_list_files(dataset)

Parameters:

dataset: Dataset identifier in format <owner>/<dataset-name>

Returns: JSON string with file details

dataset_download_files

Download dataset files to a specified location.

dataset_download_files(dataset, path="", file_name="", force=False)

Parameters:

dataset: Dataset identifier in format <owner>/<dataset-name>
path: Folder where file(s) will be downloaded (defaults to a temp directory)
file_name: File name, all files downloaded if not provided
force: Force download even if files exist

Returns: Success message or error details

dataset_metadata

Get detailed metadata for a dataset.

dataset_metadata(dataset)

Parameters:

dataset: Dataset identifier in format <owner>/<dataset-name>

Returns: JSON string with dataset metadata

dataset_create_new

Create a new dataset on Kaggle.

dataset_create_new(title, files_dir, license_name="unknown", description="", is_private=True)

Parameters:

title: Title of the dataset
files_dir: Directory containing files to upload
license_name: License for the dataset (e.g., ‘CC0-1.0’, ‘CC-BY-SA-4.0’)
description: Dataset description
is_private: Whether the dataset should be private

Returns: Success message or error details

dataset_create_version

Create a new version of an existing dataset.

dataset_create_version(dataset, files_dir, version_notes, convert_to_csv=True, delete_old_versions=False)

Parameters:

dataset: Dataset identifier in format <owner>/<dataset-name>
files_dir: Directory containing files to upload
version_notes: Notes describing the new version
convert_to_csv: Whether to convert tabular data to CSV
delete_old_versions: Whether to delete all previous versions

Returns: Success message or error details

dataset_status

Check the creation or update status of a dataset.

dataset_status(dataset)

Parameters:

dataset: Dataset identifier in format <owner>/<dataset-name>

Returns: Status information as JSON string

dataset_initialize_metadata

Initialize dataset metadata file for later upload.

dataset_initialize_metadata(path=".")

Parameters:

path: Directory where metadata file will be created

Returns: Success message or error details

dataset_update_metadata

Update metadata for an existing dataset.

dataset_update_metadata(dataset, metadata_dict)

Parameters:

dataset: Dataset identifier in format <owner>/<dataset-name>
metadata_dict: JSON string with metadata to update (title, subtitle, description, etc.)

Returns: Success message or error details

Examples

Here are some examples of how to use the dataset tools with Claude:

# Search for datasets about machine learning
datasets_list(search="machine learning", sort_by="votes")

# List files in a specific dataset
dataset_list_files("username/dataset-name")

# Download a specific dataset
dataset_download_files("username/dataset-name", "/path/to/download")

# Create a new dataset
dataset_create_new("My Dataset", "/path/to/files", license_name="CC0-1.0", description="A sample dataset", is_private=True)

# Update an existing dataset
dataset_create_version("username/dataset-name", "/path/to/files", "Updated with new data for 2023")