# GoldenCheck

Check CSV files for data quality issues � finds bad emails, phone format problems, missing values, type mismatches, outliers, and encoding errors. Zero config: just point it at a file. 19 tools: scan…

## Quick Start

```bash
# Connect this server (installs CLI if needed)
npx -y @smithery/cli@latest mcp add benzsevern/goldencheck

# Browse available tools
npx -y @smithery/cli@latest tool list benzsevern/goldencheck

# Get full schema for a tool
npx -y @smithery/cli@latest tool get benzsevern/goldencheck scan

# Call a tool
npx -y @smithery/cli@latest tool call benzsevern/goldencheck scan '{}'
```

## Direct MCP Connection

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

## Tools (19)

- `scan` — Scan a data file (CSV, Parquet, Excel) for data quality issues. Returns findings with severity, confidence, affected ro…
- `validate` — Validate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique,…
- `profile` — Profile a data file and return column-level statistics: type, null%, unique%, min/max, top values, detected formats. Al…
- `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 …
- `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,…
- `auto_configure` — Scan a data file, triage findings by confidence, and generate goldencheck.yml content from the pinned findings. Optiona…
- `explain_finding` — Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile f…
- `explain_column` — Get a natural-language health narrative for a specific column. Scans the file, profiles the column, and explains all fi…
- `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-fitti…
- `suggest_fix` — Preview fixes for a data file without applying them. Shows what would change (columns, fix types, rows affected, before…
- `pipeline_handoff` — Generate a structured quality attestation JSON for a data file. Includes health score, findings summary, pinned rules, …
- `review_stats` — Get review queue statistics for a job — counts of pending, pinned, and dismissed items.

```bash
# Get full input/output schema for a tool
npx -y @smithery/cli@latest tool get benzsevern/goldencheck <tool-name>
```