kaggle-mcp-proxy

kaggle-mcp-proxy

A remote MCP server on Cloudflare Workers that proxies tool calls to the Kaggle API. Connect from claude.ai (or any MCP client) and run Python/R code on Kaggle's free GPU/TPU infrastructure.

Tools

| Tool | Description | |------|-------------| | kaggle_kernel_push | Create/update a kernel and start execution | | kaggle_kernel_status | Check execution status (queued/running/complete/error) | | kaggle_kernel_output | Get execution output (files + log). The log field is only populated after the kernel reaches complete/error; while queued/running it is empty. Kaggle's public REST API does not expose live execution logs (the official kaggle CLI has the same limitation). Poll kaggle_kernel_status until completion. | | kaggle_kernels_list | Search kernels | | kaggle_accelerators_list | Fetch the current list of accelerator (machineShape) values Kaggle accepts, live from kaggle-cli docs | | kaggle_run | Push code, wait for completion, return output (all-in-one) | | kaggle_datasets_list | Search datasets | | kaggle_dataset_create | Create a new dataset and upload files inline (text or base64) | | kaggle_dataset_version | Upload a new version of an existing dataset | | kaggle_dataset_status | Check processing status of a dataset | | kaggle_dataset_files_list | List files (name, size, columns) inside a dataset | | kaggle_dataset_download_url | Resolve a dataset (or single file) to a temporary signed GCS download URL — no bytes pass through the worker | | kaggle_competitions_list | Search competitions |

Uploading training data

kaggle_dataset_create and kaggle_dataset_version accept files inline:

{
  "slug": "my-training-data",
  "title": "My Training Data",
  "files": [
    { "name": "train.csv", "content": "id,label\n1,0\n2,1\n", "content_type": "text/csv" },
    { "name": "weights.bin", "content": "<base64...>", "encoding": "base64" }
  ]
}

Each file is uploaded via Kaggle's blob protocol (POST /blobs/upload → PUT createUrl), then attached when the dataset (or new version) is finalized. Because content is sent inline through the MCP request, total payload size is bounded by Cloudflare Workers' request limits (100 MB on paid plans). For larger uploads, use the official kaggle CLI directly.

GPU/TPU Accelerators

The accelerator parameter on kaggle_kernel_push / kaggle_run is a free-form string that maps 1:1 to Kaggle's machineShape API field. Pass "none" for CPU-only.

To see what Kaggle currently accepts (the list changes over time as new GPUs ship), call kaggle_accelerators_list — it fetches the canonical list live from Kaggle/kaggle-cli docs/kernels.md so no proxy redeploy is required when Kaggle adds or removes a shape.

Common shape names at the time of writing: NvidiaTeslaP100, NvidiaTeslaT4, NvidiaTeslaT4Highmem, Tpu1VmV38, TpuV6E8. Several others (A100, L4, H100, RTX Pro 6000, etc.) exist but are restricted to specific competitions or admins; Kaggle will reject the push if your account is not eligible.

Note: Kaggle removed NvidiaTeslaT4x2 from the public API. NvidiaTeslaT4Highmem is the current higher-resource T4 option.

Kaggle provides 30 hours/week of free GPU time.

Setup

Prerequisites

• Cloudflare account with Workers enabled
• GitHub account (used as OAuth provider for MCP auth)
• Kaggle account with API token

1. Clone and install

git clone https://github.com/penta2himajin/kaggle-mcp-proxy.git
cd kaggle-mcp-proxy
npm install

2. Create KV namespace

npx wrangler kv namespace create OAUTH_KV
# Update wrangler.jsonc with the returned ID

3. Create GitHub OAuth App

Go to https://github.com/settings/developers → New OAuth App:

• Homepage URL: https://<your-worker>.workers.dev
• Callback URL: https://<your-worker>.workers.dev/callback

4. Get Kaggle API token

Go to https://www.kaggle.com/settings → API → Create New API Token.

5. Set secrets and deploy

npx wrangler secret put GITHUB_CLIENT_ID
npx wrangler secret put GITHUB_CLIENT_SECRET
npx wrangler secret put COOKIE_ENCRYPTION_KEY    # any random string
npx wrangler secret put ALLOWED_USERS             # comma-separated GitHub usernames (optional)
npx wrangler secret put KAGGLE_USERNAME            # your Kaggle username
npx wrangler secret put KAGGLE_KEY                 # your Kaggle API token (KGAT_... or legacy key)

npm run deploy

6. Connect from claude.ai

1. Settings → Connectors → Add custom connector
2. Remote MCP server URL: https://<your-worker>.workers.dev/mcp
3. Leave OAuth fields empty (Dynamic Client Registration is supported)
4. Authenticate with GitHub

Environment Variables (Secrets)

| Name | Required | Description | |------|----------|-------------| | GITHUB_CLIENT_ID | Yes | GitHub OAuth App client ID | | GITHUB_CLIENT_SECRET | Yes | GitHub OAuth App client secret | | COOKIE_ENCRYPTION_KEY | Yes | Random string for cookie signing | | ALLOWED_USERS | No | Comma-separated GitHub usernames | | KAGGLE_USERNAME | Yes | Kaggle account username | | KAGGLE_KEY | Yes | Kaggle API token |

Platform compatibility

This project is built for Cloudflare Workers and tested on that platform. It may work on other MCP-compatible platforms with modifications, but no guarantees are provided.

License

MIT