Server Colab Exec

MCP server that allocates Google Colab GPU runtimes (T4/L4) and executes Python code on them. Lets any MCP-compatible AI assistant — Claude Code, Claude Desktop, Gemini CLI, Cline, and others — run GPU-accelerated code (CUDA, PyTorch, TensorFlow) without local GPU hardware.

Prerequisites

• Python 3.10+
• A Google account with access to Google Colab
• On first run, a browser window opens for OAuth2 consent. The token is cached at ~/.config/colab-exec/token.json for subsequent runs.

Installation

pip install mcp-server-colab-exec

Or run directly with uvx:

uvx mcp-server-colab-exec

Configuration

Claude Code

Add to your project's .mcp.json or ~/.claude/.mcp.json:

{
  "mcpServers": {
    "colab-exec": {
      "command": "mcp-server-colab-exec"
    }
  }
}

Or via the CLI:

claude mcp add colab-exec mcp-server-colab-exec

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "colab-exec": {
      "command": "mcp-server-colab-exec"
    }
  }
}

Gemini CLI

gemini mcp add colab-exec -- mcp-server-colab-exec

Tools

colab_execute

Execute inline Python code on a Colab GPU runtime.

Parameter	Type	Default	Description
code	string	—	Python code to execute (required)
accelerator	string	"T4"	GPU type: "T4" (free) or "L4" (premium)
timeout	int	300	Max execution time in seconds

Returns JSON with per-cell output, errors, and stderr.

colab_execute_file

Execute a local .py file on a Colab GPU runtime.

Parameter	Type	Default	Description
file_path	string	—	Path to a local .py file (required)
accelerator	string	"T4"	GPU type: "T4" (free) or "L4" (premium)
timeout	int	300	Max execution time in seconds

Security policy: file_path must be a .py file inside the current workspace (cwd).

colab_execute_notebook

Execute code and collect all generated artifacts (images, CSVs, models, etc.).

Parameter	Type	Default	Description
code	string	—	Python code to execute (required)
output_dir	string	—	Local directory for downloaded artifacts (required)
accelerator	string	"T4"	GPU type: "T4" (free) or "L4" (premium)
timeout	int	300	Max execution time in seconds

Artifacts are downloaded as a zip and extracted into output_dir. Zip members are validated before extraction to prevent path traversal and special-file writes.

Examples

Check GPU availability:

colab_execute(code="import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))")

Run nvidia-smi:

colab_execute(code="import subprocess; print(subprocess.run(['nvidia-smi'], capture_output=True, text=True).stdout)")

Train a model and download weights:

colab_execute_notebook(
    code="import torch; model = torch.nn.Linear(10, 1); torch.save(model.state_dict(), '/tmp/model.pt')",
    output_dir="./outputs"
)

Authentication

On first use, the server opens a browser window for Google OAuth2 consent. The access token and refresh token are cached at ~/.config/colab-exec/token.json. Subsequent runs use the cached token and refresh it automatically.

The OAuth2 client credentials are the same ones used by the official Google Colab VS Code extension ([email protected]). They are intentionally public.

Troubleshooting

"GPU quota exceeded" — Colab has usage limits. Wait and retry, or use a different Google account.

"Timed out creating kernel session" — The runtime took too long to start. Retry — Colab sometimes has delays during peak usage.

"Authentication failed" — Delete ~/.config/colab-exec/token.json and re-authenticate.

OAuth browser window doesn't open — Ensure you're running in an environment with a browser. For headless servers, authenticate on a machine with a browser first and copy the token file.

License

MIT