GoldenCheck

Auto-discover validation rules from data — scan, profile, health-score.

CommunityOnlineNo sign-inGlobalFreeCan modify data

What it can do

  • 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
  • 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.

What data it sees

Do you need an account

No: the server works without sign-in

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

How to connect

How to connect GoldenCheck to Claude

The server address to paste into Claude settings:

https://goldencheck--benzsevern.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect GoldenCheck to ChatGPT

The server address to paste into ChatGPT settings:

https://goldencheck--benzsevern.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect GoldenCheck to Cursor

The server address to paste into Cursor settings:

https://goldencheck--benzsevern.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (19)

Raw names from tools/list. Only developers need these.

scanScan 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.
validateValidate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique, enum, range checks).
profileProfile 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_scoreGet the health score (A-F, 0-100) for a data file. Quick summary of overall data quality.
list_checksList all available profiler checks and what they detect. No arguments needed.
get_column_detailGet detailed profile and findings for a specific column.
list_domainsList all available domain packs (healthcare, finance, ecommerce, etc.). Domain packs provide specialized semantic type definitions for specific data domains.
get_domain_infoGet detailed info about a specific domain pack — lists all semantic types, their name hints, and suppression rules.
install_domainDownload a community domain pack from the goldencheck-types repository and save it for use in future scans.
analyze_dataAnalyze 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_configureScan 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_findingExplain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context.
explain_columnGet a natural-language health narrative for a specific column. Scans the file, profiles the column, and explains all findings.
review_queueList all pending review items for a given job. Returns items that need human decision (medium-confidence findings).
approve_rejectApprove (pin) or reject (dismiss) a review queue item. Decision must be 'pin' or 'dismiss'.
compare_domainsScan a file with every available domain pack (plus base/no-domain) and compare health scores. Recommends the best-fitting domain.
suggest_fixPreview fixes for a data file without applying them. Shows what would change (columns, fix types, rows affected, before/after samples).
pipeline_handoffGenerate 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_statsGet review queue statistics for a job — counts of pending, pinned, and dismissed items.