GoldenCheck
Discover data validation rules automatically—scan datasets, profile columns, and health-score quality before you ship analytics or ML features.
Overview
GoldenCheck is a Ship-phase MCP server that auto-discovers validation rules by scanning, profiling, and health-scoring data without manual rule authoring.
What is this MCP server?
- Auto-discovers validation rules from data—no hand-written rule suite required
- Scan, profile, and health-score workflows for dataset QA
- stdio via uvx/PyPI goldencheck or remote streamable-http on Railway
- Version 1.3.0 with GitHub repo and project site documentation
- Server version 1.3.0; PyPI identifier goldencheck
- Capabilities described: scan, profile, health-score; tagline: no rules to write
- Dual deployment: stdio and streamable-http remote on Railway
Community signal: 2 GitHub stars.
What problem does it solve?
Builders ship CSV and warehouse data with hidden null spikes and drift because writing validation rules by hand does not scale.
Who is it for?
Indie builders and tiny data teams validating tables and exports before launch without maintaining a huge manual rule catalog.
Skip if: Pure content research, HR ATS automation, or heavy ETL reshape jobs—that is GoldenFlow or other tooling.
What do I get? / Deliverables
You get agent-driven scans and discovered rules plus health signals you can gate releases and fixes on.
- Auto-discovered validation rules from scanned data
- Profiling and health-score summaries usable in pre-ship QA
Recommended MCP Servers
Journey fit
Canonical shelf is Ship because GoldenCheck is about proving data quality and validation coverage before production releases, not brainstorming product ideas. Testing matches auto-discovered rules, profiling, and health scores—quality gates you run pre-release on real tables and files.
How it compares
Data validation and profiling MCP, not Greenhouse recruiting or Ben Milne essay search.
Common Questions / FAQ
Who is io.github.benseverndev-oss/goldencheck for?
Solo builders and small teams who need dataset QA and discovered validation rules from an agent without writing every check manually.
When should I use io.github.benseverndev-oss/goldencheck?
Use it in Ship before you release analytics, imports, or ML features—when you want scan, profile, and health-score passes on real data.
How do I add io.github.benseverndev-oss/goldencheck to my agent?
Add stdio MCP via uvx/PyPI package goldencheck 1.3.0 or register remote https://goldencheck-mcp-production.up.railway.app/mcp/ as streamable-http.