GoldenCheck MCP Server

by benseverndev-oss Your server? Claim it

not responding

GoldenCheck is listed as active in the registry but did not answer our last check. 346 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The author archived this repository on GitHub, meaning it is no longer maintained.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 401 · avg 116 · -42% w/w

a month agotoday

Uptime history 47 days of history · worst day 0%

47 days agonow

0.0%

Uptime 24h

0 of 92 checks

Tools

read from the server

15326 ms

Response time

average over 24h

346

Installs / week

npm and PyPI

What the code does

We read the source, 23 h ago · tools taken from the live server · rules 3dff92dd89df

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

File ships in the package but is absent from the source [пакет] goldencheck/_polars_lazy.py:1, [пакет] goldencheck/cell_quality.py:1, [пакет] goldencheck/functional_dependencies.py:1 и ещё 24

этот файл ставится пользователю, но в репозитории его нет

Runs an external command goldencheck/cli/main.py:420, [пакет] goldencheck/cli/main.py:442

                result = subprocess.run(

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 0 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	15326 ms	0 min ago

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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 0 of 92 checks got a reply (0.0%), average response time 15326 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.

The registry lists GoldenCheck as active — why does it not respond?

The official MCP registry stores what the author submitted; it does not verify that the server still runs. We check the endpoint ourselves, and this one does not answer. Catalogues that copy the registry without checking will show it as working.

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 many people use GoldenCheck?

The pypi package goldencheck was installed 346 times in the last week. Week over week that is -42%. 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.