A
licenseB
qualityC
maintenanceProvides 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.
13
MIT
agent-gpu-pool
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TDQS
B3.3/5.0
Scored across 14 tools
Disambiguation5/5
Each tool targets a distinct operation or resource, with clear separation between job management (submit, get, list, cancel, logs), artifact handling (fetch, retry, list), experiment records, and infrastructure overview (pool, workers, quotas). Even similar-sounding tools like list_jobs and list_ready_results are clearly differentiated by their purpose and description.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., submit_job, list_jobs, cancel_job, fetch_artifact, sync_quotas). The verbs are clear and uniform across the set, making it easy to predict the action of each tool from its name.
Tool Count5/5
With 14 tools covering job lifecycle, artifact management, experiment recording, and cluster monitoring, the count feels well-scoped for a scientific computing MCP server. Each tool serves a distinct purpose without redundancy, and the number is within the ideal range for maintainability.
Completeness5/5
The tool set covers the full lifecycle: job submission, retrieval, listing, cancellation, logs; artifact collection, retrieval, and listing; experiment result recording and project runs; and infrastructure monitoring like worker lists and quota sync. This is a complete surface for managing computational experiments on a GPU pool.
Maintenance
ActivityMaintained
ResponsivenessNo issues