Colab MCP

Give Codex and other MCP clients access to general-purpose Google Colab CPU/GPU runtimes from Windows, macOS, or Linux. The server uses standard MCP over stdio and does not require WSL, SSH, an open Colab browser tab, or Colab Enterprise.

Quick setup for Codex

Prerequisites: Git and uv.

macOS or Linux

git clone https://github.com/anluin/colab-mcp.git
cd colab-mcp
sh scripts/install.sh

Windows PowerShell

git clone https://github.com/anluin/colab-mcp.git
cd colab-mcp
.\scripts\install.ps1

The installer runs colab-mcp setup codex: it creates an isolated environment from the lockfile, completes the one-time Google OAuth flow, and registers the stdio server through Codex's supported codex mcp add command.

Restart Codex after setup, open /mcp, and call colab_health. The ChatGPT desktop app, Codex CLI, and IDE extension share MCP configuration on the same Codex host.

Then give the agent this exact smoke task:

Use Google Colab to start a T4 session named gpu-probe, call colab_inspect, run nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader with colab_run_command, and call colab_stop in a cleanup step even if the probe fails.

Expected evidence is a non-empty gpu list, a zero command exit code, and a final stopped session.

The same CLI can configure multiple clients in one human-run step:

uv run colab-mcp setup codex claude-desktop

Or run the steps separately:

uv run colab-mcp auth
uv run colab-mcp install codex
uv run colab-mcp install claude
uv run colab-mcp install claude-desktop
uv run colab-mcp doctor
uv run colab-mcp doctor --live

Claude Desktop is registered with an isolated uv environment, so it can update or restart while Codex continues using the repository environment for a long-running session. On Windows, setup detects both the conventional %APPDATA% configuration and Microsoft Store's virtualized Claude configuration, preferring the active packaged configuration when present.

Only auth and setup may prompt for Google authorization. serve is strictly non-interactive: if credentials expire and cannot refresh, it instructs the human to rerun colab-mcp auth and never launches OAuth inside an agent session. doctor --live performs a read-only assignments API check and reports only the count, never endpoint URLs or tokens.

An existing registration is left untouched. Add --force to an install or setup command to replace it deliberately.

Related MCP server: Colab MCP

Other MCP clients

Use mcp.example.json as a template, or print ready-to-paste JSON with uv run colab-mcp install json. Run uv run colab-mcp auth once, then configure this server command:

uv --directory /absolute/path/to/colab-mcp run --locked colab-mcp serve

Capabilities

• Create local Colab-ready notebooks.

• Allocate CPU runtimes or T4, L4, G4, H100, and A100 GPUs, subject to account entitlement and availability.

• Execute Python or complete notebooks and preserve Jupyter outputs.

• Run arbitrary programs from argument arrays with a working directory, environment overrides, timeouts, exit codes, separate stdout/stderr, and bounded output.

• Start long-running processes without blocking an MCP request; list, inspect, incrementally read, interrupt, terminate, or kill them in later requests.

• List, stat, checksum, chunk-read, atomic-write, append, create, move, and explicitly remove files and directories within a runtime-owned /content boundary.

• Inspect OS, Python, CPU, RAM, disk, GPU/VRAM, CUDA/driver, requested tools, and a bounded process snapshot without assuming any workload or framework.

• Transfer files or directory trees in bounded chunks with SHA-256 verification, incremental sync skips, staged partials, atomic publication, cleanup, and explicit overwrite limits.

• Upload datasets and download artifacts.

• Pause by checkpointing a notebook and releasing its GPU.

• Resume on a fresh runtime with the same accelerator preference and optionally rerun the notebook.

• Keep active runtimes alive while the MCP server runs.

• Release runtimes explicitly after experiments or errors.

Tools: colab_health, colab_create_notebook, colab_start, colab_sessions, colab_run_command, colab_process_start, colab_process_status, colab_process_list, colab_process_output, colab_process_signal, colab_execute, colab_execute_notebook, colab_fs_list, colab_fs_stat, colab_fs_read, colab_fs_write, colab_fs_mkdir, colab_fs_move, colab_fs_remove, colab_transfer_upload, colab_transfer_download, colab_upload, colab_download, colab_pause_notebook, colab_resume_notebook, colab_paused_notebooks, colab_reconcile, colab_stop, and colab_compute_units. Use colab_inspect after allocation to discover the actual runtime rather than assuming that a requested accelerator, executable, or CUDA version is present.

General command example

{
  "argv": ["python", "-c", "import platform; print(platform.platform())"],
  "session": "compute",
  "cwd": "/content",
  "timeout": 60
}

No shell parses argv. Remote working directories must remain under /content. Each stdout and stderr result is limited to 100 KB by default (1 MB maximum). For longer work, use colab_process_start, then poll status and consume output using the returned next_offset. Process records belong to one runtime and disappear when that ephemeral runtime is released. Every command is runtime-owned and receives a process_id. The timeout is only how long the MCP call waits: if it expires, process_continues=true and the command remains alive for later status/output/signal calls. Termination is always explicit. Python/Jupyter output is bounded to 100 KB by default (1 MB maximum) and ends with an explicit truncation marker. Detached processes retain at most 10 MB per output stream by default (configurable up to 1 GB with output_limit). They continue draining excess output so the child cannot deadlock; output reads return truncated=true and, after exit, total_bytes when retained output was capped. The handoff deadline is best-effort because each Colab kernel status/output round-trip has latency; it is not a hard real-time deadline.

Filesystem reads and writes use base64 so both text and binary content round-trip without encoding ambiguity. Calls are limited to 1 MB; use offsets and append mode for larger files. Non-append writes replace atomically. Paths are resolved on the runtime and may not escape /content. Moving onto an existing path requires overwrite=true; removing a non-empty directory requires recursive=true. Use checksum=true with colab_fs_stat to verify a completed transfer.

For host/runtime transfer, prefer colab_transfer_upload and colab_transfer_download. A directory path maps its contents beneath the destination directory; a file path maps directly to the given destination file. Defaults cap a call at 100 MB, 10,000 files, and 512 KB per chunk. Existing files with the same SHA-256 are skipped when sync=true; differing destinations require overwrite=true. Uploads stage under a unique runtime path and downloads stage beside the local destination. A failed call removes its partial file before returning an error. The shorter colab_upload and colab_download names are compatibility aliases for the same bounded, verified implementation; they do not bypass its limits.

Crash recovery and orphan cleanup

Allocated endpoints are persisted before runtime preflight, so even a double failure during startup remains recoverable. colab_reconcile compares persisted sessions with the account's live Colab assignments. Its default is read-only: it reports stale local records and live orphan endpoints. Pass forget_stale=true to remove records whose runtime is already gone. Pass release_orphans=true only when you intend to release every live assignment not owned by this colab-mcp state directory. Failures are returned per endpoint for safe retry. colab_stop is idempotent when a tracked runtime has already disappeared.

Recommended agent lifecycle

1. Check colab_health.

2. Start with a T4 unless another accelerator is required.

3. Create or execute a notebook.

4. Download important artifacts.

5. Pause to checkpoint and release compute, or stop when finished.

6. Always stop a runtime after an error if it was not already released.

Pause and resume semantics

Colab does not expose a supported suspended-VM or runtime-snapshot operation. Pause records the local notebook checkpoint and accelerator preference, then releases the runtime. Resume allocates a new runtime and can rerun the notebook.

RAM variables, ad-hoc package installs, and files left only in /content do not survive. Put installation commands in the notebook and download checkpoints before pausing.

Compute units

Google does not publish a supported API for reading the compute-unit balance of a personal Colab account. colab_compute_units returns this limitation and Google's official account-management URL. The server does not scrape undocumented browser endpoints.

Free, Pro, Pro+, and Pay As You Go personal accounts are supported. Actual GPU models, runtime length, and compute usage remain controlled by Google Colab.

Security

This MCP executes arbitrary Python and can consume the authenticated account's quota. Keep it as a local stdio server and connect only trusted clients. OAuth and runtime proxy tokens are never returned through MCP tools.

Session state defaults to ~/.config/colab-mcp. Override it with COLAB_MCP_STATE_DIR. Set COLAB_MCP_AUTH=adc only if you deliberately configured Google Application Default Credentials with the required Colab scopes.

Operational logs are single-line JSON on stderr so MCP stdout framing remains clean. Configure the threshold with COLAB_MCP_LOG_LEVEL. Graceful server shutdown cancels only local keep-alive tasks; it deliberately preserves owned assignments/processes for restart recovery. Use colab_stop or colab_reconcile for explicit quota release.

Development and validation

uv sync --locked --dev
uv run ruff format --check .
uv run ruff check .
uv run mypy src
uv run pytest -q
uv build
uv run twine check dist/*

CI runs on Ubuntu, macOS, and Windows with Python 3.12. The live integration has also been verified against a real Tesla T4: allocation, CUDA execution, notebook execution, pause/release, fresh-runtime resume, rerun, and cleanup with zero assignments remaining.

The Google Colab integration version is pinned to the live-tested release. This project imports its portable client components; it does not invoke the platform-limited CLI executable.

Further documentation: architecture, security model, troubleshooting, contributing, and release procedure. Version history and readiness evidence live in Git tags, commits, and GitHub release notes rather than duplicated version-specific repository files.

Upstream projects

• https://github.com/googlecolab/google-colab-cli

• https://github.com/googlecolab/jupyter-kernel-client

• https://github.com/modelcontextprotocol/python-sdk

Codex registration follows OpenAI's documented MCP flow: https://learn.chatgpt.com/docs/extend/mcp