kaggle-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| KAGGLE_KEY | Yes | Your Kaggle API key from your Kaggle account page | |
| KAGGLE_USERNAME | Yes | Your Kaggle username from your Kaggle account |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_kaggle_datasetsC | Searches for datasets on Kaggle matching the query using the Kaggle API. |
| download_kaggle_datasetC | Downloads files for a specific Kaggle dataset. Args: dataset_ref: The reference of the dataset (e.g., 'username/dataset-slug'). download_path: Optional. The path to download the files to. Defaults to '/datasets/'. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| generate_eda_notebook | Generates a basic EDA prompt for a given Kaggle dataset reference. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one downloads a specific dataset, while the other searches for datasets. There is no overlap in functionality, making it easy for an agent to choose the correct tool for each task without confusion.
Both tools follow a consistent verb_noun pattern (download_kaggle_dataset and search_kaggle_datasets), using snake_case and clear action verbs. This consistency makes the tool set predictable and easy to understand at a glance.
With only two tools, the server feels thin for a Kaggle integration, lacking essential operations like listing datasets, uploading data, or managing competitions. While the tools are functional, the scope is incomplete for typical Kaggle workflows, making the count too low for the domain.
The tool set is severely incomplete for a Kaggle MCP server. It covers downloading and searching datasets but misses critical operations such as uploading datasets, accessing competition data, or interacting with notebooks. This creates significant gaps that will hinder agents from performing common Kaggle tasks.