A
licenseNot graded
qualityD
maintenanceA production-grade MCP server designed for multi-tenant, authenticated, and observable AI agent systems, enabling secure tool execution across heterogeneous data sources.
64
MIT
score-studio-mcp
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TDQS
A3.5/5.0
Scored across 12 tools
Disambiguation5/5
Each tool targets a distinct resource or action: auth, workflows, datasets, models, training, evaluations, and deployments. The similar-looking start/run tools are clearly separated by their object types.
Naming Consistency4/5
Tools consistently use the scorestudio_ prefix with snake_case verb_noun names. Minor deviations like whoami and run_workflow vs start_training are easy to predict but not perfectly uniform.
Tool Count5/5
With 12 tools, the server is well-scoped for an MLOps platform covering auth, workflows, datasets, models, training, evaluations, and deployments. Each tool has a clear purpose and none feel redundant.
Completeness3/5
Core read and start operations are present, but there are gaps: models only support listing, training jobs cannot be fetched after starting, and deployment management is limited to listing and verification. Agents may hit dead ends when trying to check training results or detailed model state.
Maintenance
ActivityMaintained
ResponsivenessNo issues