GoldenCheck MCP Server

io.github.benseverndev-oss/goldencheck

answering

GoldenCheck is answering right now. Last checked 2 min ago. 892 installs a week from pypi. It exposes 19 tools. Last commit 1 May 2026.

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

The author archived this repository on GitHub, meaning it is no longer maintained.

Installs per day peak 2 995 · avg 260 · +62% w/w

a month agotoday

Uptime history 43 hours of history

43 hours agonow

100.0%

Uptime 24h

91 of 91 checks

Tools

read from the server

558 ms

Response time

average over 24h

892

Installs / week

npm and PyPI

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 2 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add goldencheck --transport http https://goldencheck-mcp-production.up.railway.app/mcp/

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "goldencheck": {
      "url": "https://goldencheck-mcp-production.up.railway.app/mcp/"
    }
  }
}

~/.codex/config.toml

[mcp_servers.goldencheck]
url = "https://goldencheck-mcp-production.up.railway.app/mcp/"

.cursor/mcp.json

{
  "mcpServers": {
    "goldencheck": {
      "url": "https://goldencheck-mcp-production.up.railway.app/mcp/"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "goldencheck": {
      "url": "https://goldencheck-mcp-production.up.railway.app/mcp/"
    }
  }
}

Available tools 19

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

explain

explain_column

Get a natural-language health narrative for a specific column. Scans the file, profiles the column, and explains all findings.

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.

review

review_queue

List all pending review items for a given job. Returns items that need human decision (medium-confidence findings).

review_stats

Get review queue statistics for a job — counts of pending, pinned, and dismissed items.

analyze

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.

approve

approve_reject

Approve (pin) or reject (dismiss) a review queue item. Decision must be 'pin' or 'dismiss'.

auto

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.

checks

list_checks

List all available profiler checks and what they detect. No arguments needed.

column

get_column_detail

Get detailed profile and findings for a specific column.

compare

compare_domains

Scan a file with every available domain pack (plus base/no-domain) and compare health scores. Recommends the best-fitting domain.

domain

get_domain_info

Get detailed info about a specific domain pack — lists all semantic types, their name hints, and suppression rules.

domains

list_domains

List all available domain packs (healthcare, finance, ecommerce, etc.). Domain packs provide specialized semantic type definitions for specific data domains.

health

health_score

Get the health score (A-F, 0-100) for a data file. Quick summary of overall data quality.

install

install_domain

Download a community domain pack from the goldencheck-types repository and save it for use in future scans.

pipeline

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).

profile

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.

scan

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.

suggest

suggest_fix

Preview fixes for a data file without applying them. Shows what would change (columns, fix types, rows affected, before/after samples).

validate

validate

Validate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique, enum, range checks).

Endpoints

URL	Transport	State	Latency	Checked
https://goldencheck-mcp-production.up.railway.app/mcp/	streamable-http	answering	637 ms	2 min ago

GoldenCheck — questions

Answers built from our own checks of this server.

What can GoldenCheck do?

It exposes 19 tools, read directly from the server on our last check. Among them: analyze_data, approve_reject, auto_configure, compare_domains, explain_column, explain_finding and 13 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

What is GoldenCheck mostly used for?

Its tools cluster around explain and review. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.

Is GoldenCheck working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 558 ms. The bar chart above shows every period we have measured.

Is GoldenCheck still maintained?

The author archived this repository on GitHub, meaning it is no longer maintained. We show this because it changes what you can expect: an unmaintained server may keep answering for months and then stop without warning.

How do I connect GoldenCheck?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does GoldenCheck need an API key?

No. GoldenCheck completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 19 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is GoldenCheck?

It answers our handshake in 558 ms on average, which is faster than 19% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.

How many people use GoldenCheck?

The pypi package goldencheck was installed 892 times in the last week. Week over week that is +62%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.

Is GoldenCheck open source?

Yes — it is published under the MIT licence, written in Python, 2 stars on GitHub and 1 open issue. The source link is on this page, so you can read exactly what it does with your data before you connect it.