gpu-router
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- AlicenseNot gradedqualityDmaintenanceEnables 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
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to orchestrate multi-system LLM training, quantization, and evaluation workloads across local, Colab, Kaggle, and SSH targets, with persistent monitoring, historical analysis, and visualization via MCP tools.MIT
- AlicenseNot gradedqualityBmaintenanceEnables approved submission, monitoring, and retrieval of Kaggle CPU/GPU jobs, including two-stage preparation-to-training workflows, experiment comparison, artifact/log access, and SMTP completion notices.MIT
- FlicenseNot gradedqualityBmaintenanceEnables LLMs to manage and run machine learning training jobs on a remote server, including syncing code, submitting experiments, monitoring progress, reading TensorBoard metrics, and receiving completion notifications.-
- AlicenseBqualityCmaintenanceProvides a persistent compute queue for AI agents, enabling them to submit hardware-aware GPU jobs, monitor execution, and retrieve results and artifacts across sessions via MCP tools.13MIT
TDQS
Scored across 8 tools
Each tool targets a distinct verb on the job lifecycle (submit, route, cancel, status, logs, fetch) or a separate resource (quota, infer). The only near-pair is gpu_route vs gpu_submit, but the descriptions make the dry-run vs actual-submit distinction explicit. No meaningful overlap remains.
Every tool uses the same gpu_<verb> snake_case pattern (gpu_cancel, gpu_quota, gpu_route, gpu_infer, gpu_submit, gpu_status, gpu_logs, gpu_fetch). Verb choices are predictable and readable. Fully consistent convention throughout.
Eight tools is well within the ideal 3-15 range for the domain. Each tool earns its place by owning a distinct operation (submit, route, cancel, status, logs, fetch, quota, infer). No redundancy or padding.
The job lifecycle is well covered: dry-run routing, submission, cancellation, status/monitoring, logs, and output retrieval, plus quota and inference. Minor gaps exist (e.g. no explicit resume/retry of a failed job or a dedicated list-jobs tool, though gpu_status covers the overview). These are workable-around, not blocking.