A
licenseA
qualityD
maintenanceMCP server that allocates Google Colab GPU runtimes (T4/L4) and executes Python code on them. Lets any MCP-compatible AI assistant run GPU-accelerated code without local GPU hardware.
3
9
MIT
mcp-colab-gpu
by mio-github
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TDQS
A4.6/5.0
Scored across 10 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: execution variants (colab_execute, colab_execute_file, colab_execute_notebook) differ in input and output handling, job management tools are separate, and Drive utilities are distinct. No overlap that would cause misselection.
Naming Consistency5/5
All tools follow the consistent 'colab_' prefix with snake_case verb_noun pattern (e.g., colab_execute, colab_poll, colab_drive_upload). No mixing of conventions.
Tool Count5/5
10 tools is well-scoped for a GPU execution server: three execution variants, job lifecycle management, Drive integration, and status/version check. Each tool earns its place without being excessive or sparse.
Completeness4/5
Covers core workflows: code execution (with file and artifact options), job management, Drive upload/download, and status. Minor gaps like lack of a tool to list Drive files or manage folders, but overall sufficient for common tasks.