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<!-- mcp-name: io.github.Galaxy-Dawn/kaggle-mcp -->

A full-featured MCP server for the Kaggle API — competitions, datasets, kernels, models, benchmarks, and discussions.

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[English](README.md) | [中文](README.zh-CN.md)

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## Why kaggle-mcp?

Kaggle provides an [official remote MCP server](https://www.kaggle.com/docs/mcp) (`https://www.kaggle.com/mcp`) covering competitions, datasets, notebooks, models, and benchmarks — a solid foundation for most Kaggle workflows.

**kaggle-mcp extends that foundation** with what the official server is missing: **10 discussion tools**. You can search discussions, browse by source type, filter competition discussions by recency, read solution write-ups, explore trending topics, and more — none of which are available in the official MCP.

It also runs locally over stdio, so there's no remote MCP dependency and no `npx mcp-remote` required.

**Use kaggle-mcp** if you need discussion tools or prefer a local stdio setup without remote dependencies.
**Use the official MCP** if you prefer OAuth 2.0 auth or want zero local installation.

<p align="center">
  <img src="assets/architecture.svg" alt="kaggle-mcp architecture" width="700">
</p>

## Quick Navigation

| Section | Description |
|---------|-------------|
| [Prerequisites](#prerequisites) | Kaggle API token setup |
| [Installation](#installation) | uvx / pip / source |
| [Configuration](#configuration) | Claude Desktop, Claude Code, VS Code, Cursor |
| [Tools (51)](#tools-51) | Competitions, Datasets, Kernels, Models, Benchmarks, Discussions |
| [Debugging](#debugging) | MCP Inspector |
| [Development](#development) | Local development setup |

## Prerequisites

A Kaggle API token is required. You can authenticate using either method:

<details>
<summary><b>Option A: API Token (recommended)</b></summary>

1. Go to https://www.kaggle.com/settings → API → Create New API Token
2. Set the environment variable:

```bash
export KAGGLE_API_TOKEN="KGAT_xxxxxxxxxxxx"
```

</details>

<details>
<summary><b>Option B: kaggle.json</b></summary>

Download the token file from Kaggle settings, it will be saved to `~/.kaggle/kaggle.json`:

```json
{"username": "your_username", "key": "your_api_key"}
```

</details>

## Installation

> **Note:** MCP servers are launched automatically by MCP clients (Claude Code, VS Code, etc.) — **you don't need to run them manually in the terminal**. The commands below are what the client uses under the hood.

### Using uvx (recommended)

No installation needed. [uvx](https://docs.astral.sh/uv/guides/tools/) will automatically download and run the server:

```bash
# Used by MCP clients internally; no need to run this yourself
uvx kaggle-mcp-server
```

### Using pip

```bash
pip install kaggle-mcp-server
```

### From source

```bash
git clone https://github.com/Galaxy-Dawn/kaggle-mcp.git
cd kaggle-mcp
uv sync
```

## Configuration

### Claude Desktop

Add to your `claude_desktop_config.json`:

<details>
<summary>Using uvx (recommended)</summary>

```json
{
  "mcpServers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}
```

</details>

<details>
<summary>Using pip</summary>

```json
{
  "mcpServers": {
    "kaggle": {
      "command": "python",
      "args": ["-m", "kaggle_mcp.server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}
```

</details>

### Claude Code

```bash
claude mcp add kaggle -- uvx kaggle-mcp-server
```

Or add to your **project's** `.mcp.json` (not `settings.json`):

```json
{
  "mcpServers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}
```

### VS Code

[![Install with UV in VS Code](https://img.shields.io/badge/VS_Code-UV-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://vscode.dev/redirect/mcp/install?name=kaggle&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22kaggle-mcp-server%22%5D%7D) [![Install with UV in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-UV-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=kaggle&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22kaggle-mcp-server%22%5D%7D)

Add to `.vscode/mcp.json` (note: the key is **`"servers"`**, not `"mcpServers"`):

```json
{
  "servers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}
```

### Cursor

Add to `.cursor/mcp.json`:

```json
{
  "mcpServers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}
```

> **Tip:** If you already have `KAGGLE_API_TOKEN` in your **shell environment** (e.g. in `.bashrc` or `.zshrc`), you can omit the `"env"` block.

## Tools (51)

### Competitions (10)

| Tool | Description |
|------|-------------|
| `competitions_list` | Search and list Kaggle competitions |
| `competition_files` | List data files for a competition |
| `competition_download` | Download competition data files |
| `competition_submit` | Submit predictions to a competition |
| `competition_submissions` | View submission history |
| `competition_leaderboard` | View leaderboard (top 20) |
| `competition_get` | Get detailed competition info |
| `competition_data_summary` | Get data files summary |
| `competition_get_submission` | Get details for a single submission |
| `competition_leaderboard_download` | Download full leaderboard as CSV |

<details>
<summary>Parameter details</summary>

1. **competitions_list** — `search`, `category`, `sort_by` (`latestDeadline`/`numberOfTeams`/`recentlyCreated`), `page`
2. **competition_files** — `competition` (URL suffix, e.g. `titanic`)
3. **competition_download** — `competition`, `file_name` (optional, empty = all files) → download URL
4. **competition_submit** — `competition`, `blob_file_tokens`, `message`
5. **competition_submissions** — `competition`
6. **competition_leaderboard** — `competition` → top 20 teams and scores
7. **competition_get** — `competition` → full details (deadline, reward, evaluation metric, etc.)
8. **competition_data_summary** — `competition` → data files summary dict
9. **competition_get_submission** — `competition`, `submission_id` (integer)
10. **competition_leaderboard_download** — `competition` → download URL for full leaderboard CSV

</details>

### Datasets (11)

| Tool | Description |
|------|-------------|
| `datasets_list` | Search and list Kaggle datasets |
| `dataset_files` | List files in a dataset |
| `dataset_download` | Download dataset files |
| `dataset_metadata` | Get dataset metadata |
| `dataset_create` | Create a new dataset |
| `file_upload` | Upload a file to Kaggle |
| `dataset_get` | Get full dataset information |
| `dataset_create_version` | Create a new dataset version |
| `dataset_update_metadata` | Update dataset title/description |
| `dataset_delete` | Delete a dataset |
| `dataset_download_file` | Download a single file from a dataset |

<details>
<summary>Parameter details</summary>

1. **datasets_list** — `search`, `sort_by` (`hottest`/`votes`/`updated`/`active`), `file_type`, `page`
2. **dataset_files** — `owner`, `dataset_slug`
3. **dataset_download** — `owner`, `dataset_slug`, `file_name` (optional) → download URL
4. **dataset_metadata** — `owner`, `dataset_slug` → metadata dict
5. **dataset_create** — `owner`, `slug`, `title`, `file_tokens` (from `file_upload`), `license_name`, `is_private`
6. **file_upload** — `file_name`, `content` → file token for use in `dataset_create`
7. **dataset_get** — `owner`, `dataset_slug` → full dataset details
8. **dataset_create_version** — `owner`, `dataset_slug`, `version_notes`, `file_tokens`
9. **dataset_update_metadata** — `owner`, `dataset_slug`, `title`, `description`
10. **dataset_delete** — `owner`, `dataset_slug`
11. **dataset_download_file** — `owner`, `dataset_slug`, `file_name` → download URL

</details>

### Kernels (9)

| Tool | Description |
|------|-------------|
| `kernels_list` | Search and list notebooks/kernels |
| `kernel_pull` | Get a notebook's source code |
| `kernel_push` | Push/save a notebook to Kaggle |
| `kernel_output` | Get kernel output download URL |
| `kernel_session_create` | Create an interactive kernel session |
| `kernel_session_status` | Get kernel session execution status |
| `kernel_session_output` | List output files from a kernel session |
| `kernel_session_cancel` | Cancel a running kernel session |
| `competition_top_kernels` | List top public kernels for a competition sorted by score |

<details>
<summary>Parameter details</summary>

1. **kernels_list** — `search`, `competition`, `dataset`, `sort_by` (`hotness`/`commentCount`/`dateCreated`/`dateRun`/`relevance`/`voteCount`), `page`
2. **kernel_pull** — `user_name`, `kernel_slug` → metadata + source code
3. **kernel_push** — `title`, `text`, `language` (`python`/`r`), `kernel_type` (`notebook`/`script`), `is_private`
4. **kernel_output** — `user_name`, `kernel_slug` → download URL
5. **kernel_session_create** — `user_name`, `kernel_slug` → session details
6. **kernel_session_status** — `user_name`, `kernel_slug` → status + failure message if any
7. **kernel_session_output** — `user_name`, `kernel_slug` → list of output files with URLs
8. **kernel_session_cancel** — `user_name`, `kernel_slug`
9. **competition_top_kernels** — `competition`, `sort_by` (`scoreDescending`/`scoreAscending`/`voteCount`/`hotness`/`dateCreated`/`dateRun`/`commentCount`), `page_size` — Note: Kaggle API does not expose score values for active competitions; scores are extracted from notebook titles where authors include them (e.g. `[0.371]`, `LB:0.95`)

</details>

### Models (10)

| Tool | Description |
|------|-------------|
| `models_list` | Search and list Kaggle models |
| `model_get` | Get detailed model information |
| `model_create` | Create a new model |
| `model_update` | Update model metadata |
| `model_delete` | Delete a model |
| `model_instances_list` | List all instances of a model |
| `model_instance_get` | Get a specific model instance |
| `model_instance_create` | Create a new model instance |
| `model_instance_versions` | List versions of a model instance |
| `model_instance_version_create` | Create a new model instance version |

<details>
<summary>Parameter details</summary>

1. **models_list** — `search`, `owner`, `sort_by` (`hotness`/`downloadCount`/`createTime`/`updateTime`), `page_size`
2. **model_get** — `owner`, `model_slug`
3. **model_create** — `owner`, `slug`, `title`, `subtitle`, `is_private`, `description`
4. **model_update** — `owner`, `model_slug`, `title`, `subtitle`, `description`
5. **model_delete** — `owner`, `model_slug`
6. **model_instances_list** — `owner`, `model_slug`
7. **model_instance_get** — `owner`, `model_slug`, `framework`, `instance_slug`
8. **model_instance_create** — `owner`, `model_slug`, `framework`, `instance_slug`, `license_name`, `is_private`
9. **model_instance_versions** — `owner`, `model_slug`, `framework`, `instance_slug`
10. **model_instance_version_create** — `owner`, `model_slug`, `framework`, `instance_slug`, `version_notes`, `file_tokens`

</details>

### Benchmarks (1)

| Tool | Description |
|------|-------------|
| `benchmark_leaderboard` | Get benchmark leaderboard |

<details>
<summary>Parameter details</summary>

1. **benchmark_leaderboard** — `owner_slug`, `benchmark_slug`, `version_number` (optional, default `0`)

</details>

### Discussions (10)

| Tool | Description |
|------|-------------|
| `discussions_search` | Search Kaggle discussions |
| `discussions_list` | List discussions for a competition/dataset |
| `discussion_detail` | Get discussion content by ID |
| `discussion_comments` | Get comments for a discussion |
| `discussion_comments_search` | Search comments across all discussions |
| `discussions_by_source` | Browse discussions by source type |
| `discussions_solutions` | Browse competition solution write-ups |
| `discussions_writeups` | Browse Kaggle write-ups by type |
| `discussions_trending` | Browse trending discussions |
| `discussions_my` | List the current user's discussions |

<details>
<summary>Parameter details</summary>

1. **discussions_search** — `query`, `sort_by` (`hotness`/`votes`/`comments`/`created`/`updated`), `source_type`, `page_size`
2. **discussions_list** — `competition`, `dataset`, `page_size`, `since_hours` (filter to last N hours), `new_only` (filter by `createTime` vs `updateTime`)
3. **discussion_detail** — `discussion_id` (integer), `competition` (recommended for accuracy)
4. **discussion_comments** — `discussion_id`, `page_size`
5. **discussion_comments_search** — `query`, `page_size`
6. **discussions_by_source** — `source_type` (`competition`/`dataset`/`kernel`/`site_forum`/`competition_solution`/`model`/`write_up`/`learn_track`/`benchmark`/`benchmark_task`), `query`, `sort_by`, `page_size`
7. **discussions_solutions** — `competition` (optional slug), `sort_by`, `page_size`
8. **discussions_writeups** — `write_up_type` (`knowledge`/`competition_solution`/`hackathon`/`personal_project`/`forum_topic`/`blog`), `query`, `page_size`
9. **discussions_trending** — `source_type` (optional), `page_size`
10. **discussions_my** — `page_size`

</details>

## Debugging

You can use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) to debug the server:

```bash
npx @modelcontextprotocol/inspector uvx kaggle-mcp-server
```

The Inspector will provide a URL to access debugging tools in your browser.

## Development

```bash
git clone https://github.com/Galaxy-Dawn/kaggle-mcp.git
cd kaggle-mcp
uv sync
```

Then configure the server in your MCP client using the local path, or test with [MCP Inspector](#debugging).

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on the [GitHub repository](https://github.com/Galaxy-Dawn/kaggle-mcp).

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

TDQS

B3.4/5.0

Scored across 51 tools

Disambiguation5/5

Every tool has a clear resource prefix (competition_, dataset_, kernel_, model_, discussion_) followed by a distinct action verb. Overlaps are minimal and resolved by descriptions (e.g., dataset_download vs. dataset_download_file). The only minor issue is kernel_output's description being a copy-paste error, but tool names are unambiguous.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern of resource_prefix + action verb (e.g., competition_get, dataset_create, discussion_comments). No mixing of styles across the entire set.

Tool Count3/5

With 51 tools, this is on the high side for a single MCP server. While each tool serves a specific purpose and the Kaggle API is broad, the number feels heavy, especially with 12+ discussion-related tools. It's borderline but still manageable for an agent.

Completeness5/5

The tool surface covers nearly all key Kaggle resources: competitions (list, get, submit, leaderboard, data), datasets (full CRUD, versions, downloads), kernels (push, pull, sessions), models (create, delete, instances, versions), discussions (search, list, comments), and file upload. Missing actions (e.g., delete submission/kernel) are minor and uncommon. Very comprehensive.

Maintenance

ActivityInactive
ResponsivenessNo issues