A
licenseNot graded
qualityD
maintenanceEnables AI agents to autonomously manage Google Colab GPU sessions, submit and monitor training jobs, and debug/fix issues via an encrypted tunnel without requiring a browser tab.
MIT
colab-mcp
by DaneshSelwal
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TDQS
D1.7/5.0
Scored across 13 tools
Disambiguation2/5
Several tools appear to serve similar purposes: run_colab_cell, run_colab_code, run_runtime_code, and execute_ml_pipeline all seem to execute code, while connect_colab and open_colab_browser_connection overlap in establishing/checking a connection. Without descriptions, it's hard for an agent to pick the right tool.
Naming Consistency3/5
Most tools follow a verb_noun snake_case pattern, but there's inconsistency: some include 'colab' (e.g., list_colab_cells) while others do not (e.g., setup_ml_workspace, fetch_remote_dataset). Also, the verbs vary widely, making the naming feel less systematic than a strict verb_noun convention.
Tool Count4/5
13 tools is within a reasonable range for a Colab-focused server. However, the presence of four overlapping execution tools inflates the count and suggests some consolidation could improve the set without losing functionality.
Completeness3/5
The tool surface covers core cell operations (read, write, run, output) and notebook saving, but lacks obvious lifecycle operations like deleting a cell or creating a notebook. The ML-related tools (setup workspace, fetch dataset, pipeline) add breadth but also introduce unclear boundaries and gaps, such as no explicit tool for managing datasets beyond fetching.
Maintenance
ActivityMaintained
ResponsivenessNo issues