Cancel an active background job.

Marks the job as cancelled and attempts to cancel the underlying asyncio task. Use colab_jobs to find active job IDs.

After cancellation:

• Verify with colab_poll(job_id) that the status is 'cancelled'.

• The Colab runtime is released automatically.

• You can then start a new background job with colab_execute.

Common issues:

• Cannot cancel a job that is already completed, failed, or cancelled.

• Use colab_jobs first if you don't have the job_id.

Args: job_id: The job identifier returned by colab_execute.

Download a file from Google Drive to a local path.

Use this to retrieve results saved to Drive by colab_execute with the drive_save parameter, or any file stored on Google Drive.

After download:

• The file is saved at the specified local_path.

• Process or inspect the file locally as needed.

Common issues:

• File not found: verify the drive_path matches the Drive folder structure (relative to MyDrive root).

• First use requires Google Drive OAuth authorization.

Args: drive_path: File path on Google Drive relative to MyDrive (e.g. 'results/model.pt' or 'colab_data/output.csv'). local_path: Local destination path where the file will be saved.

Upload a local file to Google Drive.

Use this to stage input data before running colab_execute with the drive_fetch parameter. The file is uploaded to the specified folder under MyDrive.

After upload:

• Pass the drive path in colab_execute's drive_fetch parameter to make the file available on the Colab runtime.

• Example: colab_execute(code="...", drive_fetch='{"colab_data/train.csv": "/content/train.csv"}')

Common issues:

• First use requires Google Drive OAuth authorization (browser popup).

• Nested folders (e.g. 'data/train') are created automatically.

Args: local_path: Path to the local file to upload. drive_folder: Target folder path on Google Drive (relative to MyDrive). Nested paths like 'data/train' are supported. Folders are created automatically if they don't exist. Default: "colab_data".

Execute Python code on a Google Colab GPU/TPU runtime.

Primary tool for running GPU/TPU-accelerated Python code (ML training, inference, CUDA operations). Allocates hardware, runs the code, and returns structured JSON with per-cell output, errors, and stderr.

After execution:

• If background=True, poll with colab_poll(job_id) for results.

• If OOM error occurs, reduce batch_size or upgrade to A100/H100.

• If import error, add pip install <pkg> before your main code.

• To persist outputs to Google Drive, use the drive_save parameter.

• To pre-load data from Drive, use the drive_fetch parameter.

Common issues:

• QUOTA_EXCEEDED: Colab rate limit hit. Wait a few minutes or switch to a different accelerator type.

• CUDA_ERROR: Driver mismatch or GPU init failure. Retry, or try T4.

• Only ONE background job can run at a time (Colab limitation).

Args: code: Python code to execute on the Colab runtime. accelerator: Hardware accelerator type. Default: "T4". GPU types: "T4" - NVIDIA Tesla T4 (16 GB, free-tier) "L4" - NVIDIA L4 (24 GB, Colab Pro) "A100" - NVIDIA A100 (40 GB, Colab Pro/Pro+) "H100" - NVIDIA H100 (80 GB, Colab Pro+) "G4" - NVIDIA G4 (Colab Pro+) TPU types: "V5E1" - TPU v5e-1 (Colab Pro+) "V6E1" - TPU v6e-1 (Colab Pro+) high_memory: Enable high-memory runtime (more RAM). Default: False. timeout: Max execution time in seconds (10-3600). Default: 300. background: Run in background (non-blocking). Default: False. When True, returns immediately with a job_id that can be polled via colab_poll. Incompatible with drive_fetch/drive_save. drive_fetch: JSON mapping Drive paths to Colab paths. Files are downloaded from Google Drive BEFORE your code runs. Example: '{"colab_data/train.csv": "/content/train.csv"}' Requires prior colab_drive_upload to place files on Drive. drive_save: JSON mapping Colab paths to Drive paths. Files are uploaded to Google Drive AFTER your code finishes, using a freshly obtained token (safe for long-running tasks). Example: '{"/content/model.pt": "results/model.pt"}'

Execute a local Python file on a Google Colab GPU/TPU runtime.

Reads the file contents and sends them for execution on a Colab runtime. Use this instead of colab_execute when you already have a .py script file ready to run.

After execution:

• Check exit_code and cell outputs in the response.

• If you need to collect output files (models, images), use colab_execute_notebook instead.

Common issues:

• Only .py files are accepted (security restriction).

• File must exist at the given path on the local machine.

Args: file_path: Path to a local .py file to execute on Colab. accelerator: Hardware accelerator type. Default: "T4". GPU types: "T4" - NVIDIA Tesla T4 (16 GB, free-tier) "L4" - NVIDIA L4 (24 GB, Colab Pro) "A100" - NVIDIA A100 (40 GB, Colab Pro/Pro+) "H100" - NVIDIA H100 (80 GB, Colab Pro+) "G4" - NVIDIA G4 (Colab Pro+) TPU types: "V5E1" - TPU v5e-1 (Colab Pro+) "V6E1" - TPU v6e-1 (Colab Pro+) high_memory: Enable high-memory runtime (more RAM). Default: False. timeout: Max execution time in seconds (10-3600). Default: 300.

Execute Python code on Colab GPU/TPU and collect generated artifacts.

Runs the code, then scans the runtime for output files (images, CSVs, models, etc.), zips them, and downloads to a local directory. Use this when your code produces files you need to retrieve locally.

After execution:

• Check artifact_files in the response for collected output filenames.

• Files are extracted to the specified output_dir.

• For files larger than 50 MB, use colab_execute with drive_save instead.

Common issues:

• No artifacts collected: ensure your code writes files to /tmp, /content, or the current working directory.

• Each artifact file must be under 50 MB (base64 transfer limit).

Args: code: Python code to execute on the Colab runtime. output_dir: Local directory to save the artifacts zip and extracted files. accelerator: Hardware accelerator type. Default: "T4". GPU types: "T4" - NVIDIA Tesla T4 (16 GB, free-tier) "L4" - NVIDIA L4 (24 GB, Colab Pro) "A100" - NVIDIA A100 (40 GB, Colab Pro/Pro+) "H100" - NVIDIA H100 (80 GB, Colab Pro+) "G4" - NVIDIA G4 (Colab Pro+) TPU types: "V5E1" - TPU v5e-1 (Colab Pro+) "V6E1" - TPU v6e-1 (Colab Pro+) high_memory: Enable high-memory runtime (more RAM). Default: False. timeout: Max execution time in seconds (10-3600). Default: 300.

List all tracked background jobs.

Returns a JSON array of job summaries including job_id, status, accelerator type, and timestamps. Use this to find job IDs for colab_poll or colab_cancel.

After listing:

• Use colab_poll(job_id) on any job to get detailed results.

• Use colab_cancel(job_id) to stop an active job.

• Completed/failed/cancelled jobs are auto-cleaned after 5 minutes.

Poll a background job for its current status and results.

Use this after launching a background execution with colab_execute(..., background=True) to check progress and retrieve results when complete.

Interpreting the response:

• status='starting' or 'running': job is in progress, poll again later.

• status='completed': result field contains the execution output.

• status='failed': error field describes what went wrong.

• status='cancelled': job was stopped via colab_cancel.

Common issues:

• Unknown job_id: use colab_jobs to list all tracked jobs.

• Jobs are cleaned up automatically after 5 minutes.

Args: job_id: The job identifier returned by colab_execute.

Return current server status including accelerator info and job state.

Use this before executing code to check which accelerators are available and whether a background job is already running (only one background job is allowed at a time).

After checking status:

• Choose an accelerator from supported_accelerators for colab_execute.

• If active_job is present, wait for it or cancel with colab_cancel before starting a new background job.

Return the mcp-colab-gpu server version.

Use this to verify server compatibility or for debugging.