# GoldenCheck (remote · goldencheck-mcp-production.up.railway.app)

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

- Trust score: 68/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `goldencheck-mcp-production.up.railway.app`: 68/100 (this document), [markdown](https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck-mcp-production.md), [page](https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck-mcp-production)
- pypi · `goldencheck`: 21/100, [markdown](https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck.md), [page](https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck)

## Channel facts

- Endpoint: `https://goldencheck-mcp-production.up.railway.app/mcp/`
- Transports: `streamable-http`
- Auth: `none`
- Version: `3.2.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 57/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 19 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 79/100
  - AI-judged instruction clarity (excellent).
  - Tool/resource definitions use about 1160 tokens (~61/item across 19 items; 19 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http benseverndev-oss-goldencheck https://goldencheck-mcp-production.up.railway.app/mcp/
```

### Codex

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

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "benseverndev-oss-goldencheck": {
      "type": "remote",
      "url": "https://goldencheck-mcp-production.up.railway.app/mcp/",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add benseverndev-oss-goldencheck --url https://goldencheck-mcp-production.up.railway.app/mcp/ --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  benseverndev-oss-goldencheck:
    url: "https://goldencheck-mcp-production.up.railway.app/mcp/"
```

### Other

```json
{
  "mcpServers": {
    "benseverndev-oss-goldencheck": {
      "type": "http",
      "url": "https://goldencheck-mcp-production.up.railway.app/mcp/"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 68, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-01 (score 67, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 66, −1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 67, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-29 (score 66, +1)

No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 65, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 64)

First indexed and scored.

## MCP tools (19)

### `scan` (~127 tokens)

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.

Input parameters:

- `file_path` (string, required): Path to the data file (CSV, Parquet, Excel)
- `llm_boost` (boolean): Enable LLM enhancement (requires API key env var)
- `llm_provider` (string): LLM provider: 'anthropic' or 'openai'
- `sample_size` (integer): Max rows to sample (default 100000)

### `validate` (~69 tokens)

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

Input parameters:

- `config_path` (string): Path to goldencheck.yml (default: ./goldencheck.yml)
- `file_path` (string, required): Path to the data file

### `profile` (~76 tokens)

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.

Input parameters:

- `file_path` (string, required): Path to the data file
- `sample_size` (integer): Max rows to sample (default 100000)

### `health_score` (~46 tokens)

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

Input parameters:

- `file_path` (string, required): Path to the data file

### `list_checks` (~22 tokens)

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

### `get_column_detail` (~43 tokens)

Get detailed profile and findings for a specific column.

Input parameters:

- `column` (string, required): Column name to inspect
- `file_path` (string, required): Path to the data file

### `list_domains` (~35 tokens)

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

### `get_domain_info` (~50 tokens)

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

Input parameters:

- `domain` (string, required): Domain pack name (e.g., healthcare, finance, ecommerce)

### `install_domain` (~57 tokens)

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

Input parameters:

- `domain` (string, required): Domain pack name to install
- `output_path` (string): Output path (default: goldencheck_domain.yaml)

### `analyze_data` (~63 tokens)

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.

Input parameters:

- `file_path` (string, required): Path to the data file (CSV, Parquet, Excel)

### `auto_configure` (~79 tokens)

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.

Input parameters:

- `constraints` (object): Optional constraints: {min_confidence, severity_filter, include_columns, exclude_columns}
- `file_path` (string, required): Path to the data file

### `explain_finding` (~80 tokens)

Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context.

Input parameters:

- `file_path` (string, required): Path to the data file (needed for profile context)
- `finding` (object, required): Finding dict with keys: severity, column, check, message, affected_rows, confidence, sample_values

### `explain_column` (~57 tokens)

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

Input parameters:

- `column` (string, required): Column name to explain
- `file_path` (string, required): Path to the data file

### `review_queue` (~43 tokens)

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

Input parameters:

- `job_name` (string, required): Job name to filter review items

### `approve_reject` (~80 tokens)

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

Input parameters:

- `decision` (string, required): Decision: 'pin' (approve) or 'dismiss' (reject)
- `item_id` (string, required): Review item ID to update
- `reason` (string): Optional reason for the decision

### `compare_domains` (~48 tokens)

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

Input parameters:

- `file_path` (string, required): Path to the data file

### `suggest_fix` (~69 tokens)

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

Input parameters:

- `file_path` (string, required): Path to the data file
- `mode` (string): Fix mode: 'safe' (default) or 'aggressive'

### `pipeline_handoff` (~76 tokens)

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).

Input parameters:

- `file_path` (string, required): Path to the data file
- `job_name` (string, required): Job name for the handoff record

### `review_stats` (~40 tokens)

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

Input parameters:

- `job_name` (string, required): Job name to get stats for

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck-mcp-production#diagnostics

## Score history

- 2026-08-03: 68
- 2026-08-02: 67
- 2026-08-01: 67
- 2026-07-31: 66
- 2026-07-30: 67
- 2026-07-29: 66
- 2026-07-28: 65
- 2026-07-27: 65
- 2026-07-26: 64

## Links

- Remote endpoint: https://goldencheck-mcp-production.up.railway.app/mcp/
- Repository: https://github.com/benseverndev-oss/goldencheck
- Website: https://benseverndev-oss.github.io/goldencheck/
- Changelog RSS feed: https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck-mcp-production/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck-mcp-production/changelog.json
- HTML version of this page: https://verifymcp.io/servers/benseverndev-oss-goldencheck/goldencheck-mcp-production