GoldenCheck

Auto-discover validation rules from data — scan, profile, health-score. No rules to write.

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

模型生成摘要（rules/v1 · 2026-09-27 16:41）：Auto-discover validation rules from data — scan, profile, health-score
这是模型对公开材料的总结，不是官方声明，请以原链内容为准。

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://goldencheck--benzsevern.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "GoldenCheck": {
      "url": "https://goldencheck--benzsevern.run.tools"
    }
  }
}

能力清单

工具	说明
scan	Scan a data file (CSV, Parquet, Excel) for data quality issues. Returns findings with severity, confidence, affected rows, and sample values. No configuration needed — rules are discovered from the data.
validate	Validate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique, enum, range checks).
profile	Profile a data file and return column-level statistics: type, null%, unique%, min/max, top values, detected formats. Also returns a health score (A-F) based on finding severity.
health_score	Get the health score (A-F, 0-100) for a data file. Quick summary of overall data quality.
list_checks	List all available profiler checks and what they detect. No arguments needed.
get_column_detail	Get detailed profile and findings for a specific column.
list_domains	List all available domain packs (healthcare, finance, ecommerce, etc.). Domain packs provide specialized semantic type definitions for specific data domains.
get_domain_info	Get detailed info about a specific domain pack — lists all semantic types, their name hints, and suppression rules.
install_domain	Download a community domain pack from the goldencheck-types repository and save it for use in future scans.
analyze_data	Analyze a data file to detect its domain, profile columns, and recommend a scanning strategy. Returns domain detection, column count, row count, strategy decisions, and alternative approaches.
auto_configure	Scan a data file, triage findings by confidence, and generate goldencheck.yml content from the pinned findings. Optionally accepts constraints to filter or adjust the generated config.
explain_finding	Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context.
explain_column	Get a natural-language health narrative for a specific column. Scans the file, profiles the column, and explains all findings.
review_queue	List all pending review items for a given job. Returns items that need human decision (medium-confidence findings).
approve_reject	Approve (pin) or reject (dismiss) a review queue item. Decision must be 'pin' or 'dismiss'.
compare_domains	Scan a file with every available domain pack (plus base/no-domain) and compare health scores. Recommends the best-fitting domain.
suggest_fix	Preview fixes for a data file without applying them. Shows what would change (columns, fix types, rows affected, before/after samples).
pipeline_handoff	Generate a structured quality attestation JSON for a data file. Includes health score, findings summary, pinned rules, and attestation status (PASS, PASS_WITH_WARNINGS, REVIEW_REQUIRED, FAIL).
review_stats	Get review queue statistics for a job — counts of pending, pinned, and dismissed items.

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