A
licenseB
qualityB
maintenanceEnables AI agents to submit GPU job requests, select eligible workers, track execution, and retrieve results via MCP.
14
21
MIT
Badgr — GPU Compute for AI Agents Copy
Officialby AiBadgr
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TDQS
B3.1/5.0
Scored across 11 tools
Disambiguation1/5
The set contains exact duplicate purposes: badgr_run and run_workload both run a one-off GPU job with identical descriptions, and badgr_serve and deploy_model both provision a persistent endpoint identically. badgr_status and check_result also overlap on deployment state inspection, making misselection highly likely.
Naming Consistency2/5
Naming mixes a badgr_ prefix (badgr_run, badgr_status, badgr_logs) with unrelated generic verb_noun names (run_workload, deploy_model, check_result) and an unaffixed ai_chat. The duplication across naming schemes makes the surface feel inconsistent rather than deliberately organized.
Tool Count3/5
Eleven tools is a reasonable raw count for the GPU compute domain, but two pairs are exact duplicates, so the effective surface is smaller and the count overstates the real capability. The set would be better scoped if duplicates were removed.
Completeness4/5
The lifecycle is broadly covered: run jobs, serve endpoints, check status/logs, tear down billing, call chat, and launch/check autonomous agent tasks. Minor gaps exist around pricing/model discovery or richer cost inspection, but core workflows are reachable.
Maintenance
ActivityMaintained
ResponsivenessSyncing