mcp server
GoldenCheck
Auto-discover validation rules from data — scan, profile, health-score. No rules to write.
Description as published by the maintainer. Source
- version 3.2.0
- archived
archived — The owner archived the repository. It will not receive fixes.
What this server can do
19 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
analyze_data(file_path)- 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. Required: file_path.
approve_reject(reason, item_id, decision)- Approve (pin) or reject (dismiss) a review queue item. Decision must be 'pin' or 'dismiss'. Required: item_id, decision.
auto_configure(file_path, constraints)- 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. Required: file_path.
compare_domains(file_path)- Scan a file with every available domain pack (plus base/no-domain) and compare health scores. Recommends the best-fitting domain. Required: file_path.
explain_column(column, file_path)- Get a natural-language health narrative for a specific column. Scans the file, profiles the column, and explains all findings. Required: file_path, column.
explain_finding(finding, file_path)- Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context. Required: file_path, finding.
get_column_detail(column, file_path)- Get detailed profile and findings for a specific column. Required: file_path, column.
get_domain_info(domain)- Get detailed info about a specific domain pack — lists all semantic types, their name hints, and suppression rules. Required: domain.
health_score(file_path)- Get the health score (A-F, 0-100) for a data file. Quick summary of overall data quality. Required: file_path.
install_domain(domain, output_path)- Download a community domain pack from the goldencheck-types repository and save it for use in future scans. Required: domain.
list_checks- List all available profiler checks and what they detect. No arguments needed.
list_domains- List all available domain packs (healthcare, finance, ecommerce, etc.). Domain packs provide specialized semantic type definitions for specific data domains.
pipeline_handoff(job_name, file_path)- 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). Required: file_path, job_name.
profile(file_path, sample_size)- 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. Required: file_path.
review_queue(job_name)- List all pending review items for a given job. Returns items that need human decision (medium-confidence findings). Required: job_name.
review_stats(job_name)- Get review queue statistics for a job — counts of pending, pinned, and dismissed items. Required: job_name.
scan(file_path, llm_boost, sample_size, llm_provider)- 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. Required: file_path.
suggest_fix(mode, file_path)- Preview fixes for a data file without applying them. Shows what would change (columns, fix types, rows affected, before/after samples). Required: file_path.
validate(file_path, config_path)- Validate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique, enum, range checks). Required: file_path.
Last successful function declaration observed on . Source: https://goldencheck-mcp-production.up.railway.app/mcp/. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://goldencheck-mcp-production.up.railway.app/mcp/.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 2 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-05-01 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 1 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 3.2.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-07-16 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-07-16 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | archived | The owner archived this repository on GitHub. It is read-only and will not receive fixes. | as of fetch | GitHub | |
| mcp tools declared | 19 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | goldencheck-mcp-production.up.railway.app | |
| mcp endpoint status | ok | The server listed 19 functions when asked. | as of probe | goldencheck-mcp-production.up.railway.app |
Where to get it
Related, by what their authors tagged them
-
GoldenAnalysis
— last commit 2026-08-06, shares data-engineering, data-quality, polars
Read-only cross-cutting analysis, metrics, and reporting across the Golden Suite.
-
GoldenMatch
— last commit 2026-08-06, shares data-engineering, data-quality, polars
Find duplicate records in 30 seconds. Zero-config entity resolution, 97.2% F1 out of the box.
-
andon
— last commit 2026-08-04, shares cli, data-quality, data-validation
Deterministic verification for AI-generated analysis: run a spec, inspect or diff a workbook
-
io.github.haiiibin/data-profiler-mcp
— last commit 2026-07-30, shares csv, data-quality, parquet
Profiles CSV/Parquet/Excel/JSON files: schema, stats, quality flags and dtype tips for LLM agents.
-
io.github.ar-agents/mcp
— last commit 2026-08-03, shares edge-runtime
Argentine business automation: Mercado Pago, AFIP/ARCA, WhatsApp, banking, shipping (7 packages).
-
GoldenFlow
— archived, last commit 2026-05-01, shares a2a, agent, cli
Standardize, reshape, and normalize messy data — CSV, Excel, Parquet, S3, databases.
-
GoldenPipe
— archived, last commit 2026-05-01, shares a2a, agent, cli
One command to validate, transform, and deduplicate — chain GoldenCheck + Flow + Match.
-
InferMap
— archived, last commit 2026-05-01, shares cli, data-engineering, data-quality
Map messy columns to a known schema — 7 scorers, domain dictionaries, F1 0.84. Zero config.
-
io.github.arpe-io/fastbcp-mcp
— last commit 2026-03-18, shares data-engineering, parquet
MCP server for FastBCP — high-performance parallel database export to files and cloud
-
io.github.arpe-io/lakexpress-mcp
— last commit 2026-03-18, shares data-engineering, parquet
MCP server for LakeXpress — automated database-to-cloud data pipeline as Parquet
These share tags the maintainers applied themselves, such as data-engineering, data-quality, polars, zero-config. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: a2a, agent, cli, csv, data-engineering, data-quality, data-validation, edge-runtime, javascript, llm, mcp-server, nodejs, npm, parquet, polars, profiling, python, tui, typescript, zero-config.
This record as data
Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.
GET /api/v1/entries/mcp_server.json