AI training data quality assessment, bias detection, and governance scoring are delivered to any MCP-compatible AI agent through a single always-on server. This server orchestrates 7 specialized data sources including dataset registries, GitHub, ArXiv, Semantic Scholar, Hacker News, Wikipedia, and Data.gov to produce per-dataset quality scores, bias indicator reports, provenance chains, governance grades, trend rankings, and model-data fit assessments. The result is a complete intelligence layer for AI teams needing to understand, audit, and defend their training data choices, with every tool call querying multiple sources in parallel, building a cross-referenced data network, and running weighted scoring algorithms to surface the best data for your model without requiring API keys or configuration.
주요 기능
017-source parallel querying for comprehensive data retrieval
027-type bias detection with 15+ keyword patterns and severity levels
03Weighted composite quality scoring across 5 dimensions
040 GitHub stars
058 specialized tools covering data evaluation lifecycle
06License scoring matrix for 20+ license types and AI training openness
