Colab Exec MCP server

mcp-server-colab-exec

<!-- mcp-name: io.github.pdwi2020/mcp-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 claudedesktopconfig.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.

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

colabexecutefile

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

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

colabexecutenotebook

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

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.

Shortened. The full README is on GitHub.