mcp server
GoldenAnalysis
Read-only cross-cutting analysis, metrics, and reporting across the Golden Suite.
Description as published by the maintainer. Source
- version 0.4.0
- active
- analytics
active — Most recent push to the repository was 2026-08-06. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
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 | 128 | 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 | 2026-08-06 | 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 | 11 | 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 | 0.4.0 | 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 | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
GoldenMatch
— last commit 2026-08-06, shares data-cleaning, data-engineering, data-matching
Find duplicate records in 30 seconds. Zero-config entity resolution, 97.2% F1 out of the box.
-
GoldenCheck
— archived, last commit 2026-05-01, shares data-engineering, data-quality, polars
Auto-discover validation rules from data — scan, profile, health-score. No rules to write.
-
GoldenFlow
— archived, last commit 2026-05-01, shares data-cleaning, data-engineering, data-quality
Standardize, reshape, and normalize messy data — CSV, Excel, Parquet, S3, databases.
-
InferMap
— archived, last commit 2026-05-01, shares data-engineering, data-quality, fuzzy-matching
Map messy columns to a known schema — 7 scorers, domain dictionaries, F1 0.84. Zero config.
-
GoldenPipe
— archived, last commit 2026-05-01, shares data-engineering, data-quality, polars
One command to validate, transform, and deduplicate — chain GoldenCheck + Flow + Match.
-
TrustyData
— last commit 2026-07-13, shares data-quality
French address quality, geocoding & routing from official data (BAN, INSEE, OpenStreetMap).
-
io.github.agenson-horrowitz/agent-output-guard
— last commit 2026-04-05, shares data-quality
Validate and verify data from other agents before acting on it. Zero LLM costs.
-
TrainTools
— last commit 2026-07-22, shares data-quality
Recommend paper-backed diagnostics for PyTorch and Hugging Face training problems.
-
io.datanika/datanika-mcp
— last commit 2026-07-23, shares data-engineering
Read-only-by-default MCP for Datanika: browse data, run dbt transforms, manage ELT pipelines.
-
io.github.AnnasMazhar/pyspark-mcp
— last commit 2026-08-03, shares data-engineering
SQL to PySpark conversion, AWS Glue job generation, and Spark code optimization.
These share tags the maintainers applied themselves, such as data-cleaning, data-engineering, data-matching, data-quality. 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: data-cleaning, data-engineering, data-matching, data-quality, deduplication, entity-resolution, fellegi-sunter, fuzzy-matching, knowledge-graph, llm, master-data-management, mcp-server, polars, pprl, python, record-linkage, rust, splink, typescript, zero-config.
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