The Problem
Connecting desktop AI assistants (like Gemini CLI) to Google Colab runtimes requires custom glue code. Each integration needs to handle authentication, session management, and websocket communication with Colab's backend. That work is repetitive and error-prone. colab-mcp packages it as a standard MCP server so any MCP-compatible client can drive Colab notebooks through a uniform interface.
What This Does
colab-mcp is a Python MCP server that exposes Colab operations as MCP tools. The core modules in src/colabmcp/ split responsibilities cleanly: auth.py handles Google authentication, session.py manages Colab runtime sessions, runtime.py wraps runtime lifecycle operations, client.py provides the Colab client interface, and websocketserver.py runs the MCP transport layer. The package entry point lives in src/colabmcp/init.py.
The repo includes five test files in tests/ mirroring each module, plus pyproject.toml for packaging and uv.lock for dependency pinning. A .githooks/pre-commit hook and .pre-commit-config.yaml enforce local quality gates. The README documents both end-user setup via uvx and internal developer workflow.
How To Use It
Setup: Install uv first (pip install uv), then configure your MCP client. The README shows the canonical mcp.json snippet using uvx to fetch the package directly from GitHub. A note for Google-internal users: add --index https://pypi.org/simple if you have a non-standard package index.
Configuration: No environment variables or config files are required for basic use. Authentication happens through Google's standard OAuth flow handled in src/colabmcp/auth.py.
Running it: The server is invoked through your MCP client's configuration. For end users, the README shows this mcp.json snippet:
{ "mcpServers": { "colab-mcp": { "command": "uvx", "args": ["git+https://github.com/googlecolab/colab-mcp"], "timeout": 30000 } } }
For developers working from a checkout, the README documents running uv run colab-mcp with a cwd pointing at the repo.
Real-World Use
A data scientist using Gemini CLI wants to execute a Colab notebook from their terminal. With colab-mcp configured as an MCP server, they can ask the assistant to "run the training notebook and report the final loss" without manually opening Colab in a browser, copying URLs, or handling auth tokens. The MCP server handles session creation, code execution, and result retrieval through the standard MCP tool interface.
Code Health & Issues
Med - No CI/CD pipeline - No .github/workflows/ or equivalent CI config exists. Tests run only via local pre-commit hooks, so regressions can slip through on push. Low - Lockfile present but no build gate - uv.lock exists for reproducible installs, but nothing enforces it in CI (see above). Low - Dependency hygiene - pyproject.toml declares dependencies without a lockfile committed for consumers; uv.lock helps developers but external users installing via uvx get floating versions. Positive - Five test files cover each module, pre-commit hooks enforce linting/formatting, and the codebase is small (11 Python files) with clear module separation.
The Bottom Line
A clean, focused MCP server that solves a real integration problem for Colab users. The code is well-structured and tested for its size, but the missing CI is a real gap for a project that will receive external contributions. Suitable for anyone using MCP-compatible assistants with Colab; the Google-internal notes in the README suggest it's primarily built for that audience, but the public setup path works for anyone.