DataSentry

Name: DataSentry
Author: Jackxiaozhiren

by Jackxiaozhirenversion 2.0.0i★ –iRuns on your computer

Registry nameio.github.Jackxiaozhiren/datasentry

Install

Overview

Description from the maker (original English)

Local-first MCP server for data-quality discovery, evidence, drift, and controlled repair.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "datasentry": {
      "command": "uvx",
      "args": [
        "datasentry-ai",
        "datasentry",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add datasentry -- uvx datasentry-ai datasentry mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "datasentry": {
      "command": "uvx",
      "args": [
        "datasentry-ai",
        "datasentry",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "datasentry": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "datasentry-ai",
        "datasentry",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Jackxiaozhiren, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32497 servers in this category
Published to the registry more recently than 67% of the other 32497 servers in this category
Basic checks equal to or better than 100% of the other 32497 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is DataSentry safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.0.0) reached the registry 18 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Zoo MCP

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

DataSentry

Find bad data before your users do.

Automatic data-quality discovery, evidence-backed explanations, and safe reversible repair.

Local-first. Deterministic by default. AI optional.

Live demo · 30-second start · Use cases · FAQ · PyPI · Examples · MCP setup · Contribute

中文导读：DataSentry 会先自动发现数据质量问题，再给出样本、比例、置信度等证据。修复采用 propose → preview → apply to a copy → verify → rollback 的保守流程。检测与评分不依赖 LLM，AI 只作为可选辅助，数据可以完全留在本机。

Try it in 30 seconds

Install the current PyPI release and run the zero-config product tour:

pip install --upgrade datasentry-ai

datasentry demo

datasentry-demo is also available as a direct console alias.

The demo generates synthetic dirty data, runs the built-in detectors, exports JSON + HTML reports, applies one safe repair to a copy, re-scans the repaired copy, and prints a rollback command. It needs no dataset, cloud service, API key, or LLM.

synthetic dirty CSV

↓ 39 deterministic detectors ↓ evidence-backed issues + quality score ↓ preview → repaired copy ↓ re-scan → verify new/persistent issues

Prefer scanning your own data immediately?

curl -L https://raw.githubusercontent.com/Jackxiaozhiren/datasentry/main/demo-data/orders.csv -o orders.csv

datasentry scan orders.csv datasentry issues list --severity high

Or launch the interactive interfaces:

datasentry          # terminal UI

datasentry-server # Web UI + REST API at http://localhost:8000/ui/

Start with your problem

If you need to...	Start here
Find suspicious data before you know every rule	`datasentry scan data.csv`
See the full discovery → repair → verify loop	`datasentry demo`
Block severe data issues in CI	`docs/GITHUB_ACTIONS.md`
Add quality gates to dbt / Airflow	`examples/integrations/`
Inspect and repair issues without overwriting the source	`Safe repair`
Give AI agents deterministic data-quality tools	`docs/MCP.md`

The default path is intentionally low-commitment: scan first, inspect evidence, then decide whether a finding deserves a permanent rule or a repair.

Why DataSentry exists

Most data-quality tools are excellent once you already know the expectations, checks, or contracts you want to enforce. Real incidents often start one step earlier: you do not yet know what is wrong.

DataSentry is built around the complete remediation loop:

Find → Explain → Fix safely → Verify

Find — discover common quality problems without writing every rule first.
Explain — attach samples, affected counts/ratios, detector evidence, and confidence.
Fix safely — preview changes and apply repairs to a copy instead of mutating the source.
Verify — re-scan the repaired copy and surface persistent or newly introduced issues.

DataSentry fits best when you are looking at data you do not fully trust yet, want evidence before codifying checks, or need a conservative remediation path. It complements expectation- and contract-driven tools rather than requiring you to replace them.

What it catches automatically

DataSentry ships with 39 deterministic detectors covering common failure modes such as:

missing and placeholder values;
invalid emails, URLs, dates, and encodings;
duplicate identifiers and uniqueness violations;
inconsistent categories and cross-field contradictions;
foreign-key and referential-integrity problems;
numeric and statistical outliers;
schema, row-count, score, and issue-distribution drift.

Every scan produces an evidence-backed issue list and a six-dimension quality score across completeness, validity, uniqueness, consistency, integrity, and timeliness.

Safe repair, not blind mutation

# inspect the highest-severity findings

datasentry issues list --severity high

propose a repair without changing data

datasentry repair propose --file orders.csv

preview the exact effect

datasentry repair preview --file orders.csv

apply to a repaired copy; the original is not overwritten

datasentry repair apply --file orders.csv

re-scan the repaired copy and detect regressions

datasentry repair verify

inspect or undo the repair

datasentry repair diff datasentry repair rollback

Repairs are fingerprinted, auditable, and reversible. AI-generated repair proposals remain human-approved state changes.

Quality gates for CI

datasentry scan orders.csv --fail-on high

For GitHub repositories, reuse DataSentry's maintained workflow_call gate instead of duplicating installation and exit-code handling:

jobs:

datasentry: uses: Jackxiaozhiren/datasentry/.github/workflows/datasentry-quality-gate.yml@main with: path: data/orders.csv fail_on: high

See docs/GITHUB_ACTIONS.md for inputs, artifacts, security boundaries, and version-pinning guidance.

Reports can be exported as JSON, Markdown, HTML, JUnit, and SARIF. The versioned, machine-readable JSON report Schema is included for downstream validation. The GitHub Actions example fails the workflow on severe findings while still uploading an HTML report for review.

Give AI agents deterministic data-quality tools

DataSentry includes an MCP stdio server:

datasentry mcp --project /path/to/project

MCP-capable clients can scan files, inspect evidence-backed issues, read quality scores and trends, compare drift, validate contracts, manage scheduled jobs, and call DataSentry tools without bypassing the same underlying safety rules used by the CLI and REST API.

Copy-paste setup recipes for VS Code and Claude Desktop are in docs/MCP.md.

Boundary: AI may propose; humans approve state-changing repairs.

Where it fits next to popular data-quality projects

This is a positioning guide, not a winner/loser feature scorecard. These projects solve overlapping but different jobs; check their upstream documentation for current capabilities.

Project	Core mental model	A strong fit when you want...
DataSentry	discover → explain → repair → verify	automatic issue discovery plus a controlled, reversible remediation loop
Great Expectations	Expectations / expressive data tests	explicit validation rules, validation results, and generated data-quality documentation
Soda Core	data contracts and quality checks	YAML contracts and verification across a broad data stack
Deequ	“unit tests for data” on Spark	large-scale data verification in Spark-centric environments
ydata-profiling	one-line profiling / EDA	fast exploratory profiling and shareable analysis reports

DataSentry is intentionally not trying to replace a metadata catalog, lineage platform, or every validator. Its focus is narrower: find bad data, show why it was flagged, and close the repair loop without gambling on the source.

Local-first by design

deterministic detection and scoring run locally;
DuckDB powers core local execution;
OpenAI/Ollama assistance is optional;
PII redaction, encrypted mappings, and LLM audit records are available when AI is enabled;
the original source file is not overwritten by repair workflows.

Data sources

CSV, Parquet, JSONL, XLSX
DuckDB and SQLite
PostgreSQL and MySQL
s3://, gs://, and az:// objects
single files, batches, and globs

History and drift

Persist scans and compare data over time:

datasentry drift latest orders

datasentry score

Tracked signals include schema changes, row-count movement, quality-score changes, and issue-distribution drift.

Architecture

flowchart LR

Sources[Files / DBs / cloud objects] --> DuckDB[Local execution] DuckDB --> Detect[39 detectors] Detect --> Evidence[Evidence fusion] Evidence --> Score[6-dimension score] Score --> Reports[Reports / history / gates] Reports --> CLI[CLI / TUI] Reports --> Web[Web / REST] Reports --> MCP[MCP] Evidence --> Proposal[Repair proposal] Proposal --> Preview[Preview] Preview --> Apply[Apply to copy] Apply --> Verify[Verify by re-scan] Verify --> Rollback[Rollback artifact] LLM[Optional OpenAI / Ollama] -. proposes .-> Proposal

Reproducible benchmark

uv sync

uv run python benchmarks/bench_scan.py 1000000 42

The benchmark generates synthetic dirty data and measures profiling, detection/fusion/scoring, numeric-outlier detection, JSONL reading, sampling, score drift, and memory high-water marks. See docs/BENCHMARKS.md.

Development

uv sync

make check # lint + mypy --strict + tests/coverage make demo # exercise the public datasentry demo path make bench # benchmark make build # distributions

Contributing

Useful contributions include new detectors, connectors, integration examples, reproducible benchmark cases, documentation/translations, minimal bug reproductions, and CLI/TUI/Web usability improvements.

Want a small first contribution? Start with good first issue #14 or good first issue #15.

See CONTRIBUTING.md, CODE_OF_CONDUCT.md, SECURITY.md, and ROADMAP.md. Small contributions are tracked with `good first issue` and `help wanted` labels.

Documentation

Project site and live report
docs/FAQ.md — evaluation, safety boundaries, and how DataSentry fits alongside other data-quality tools
examples/ — scenario-first runnable examples
docs/MCP.md — VS Code and Claude Desktop MCP setup
docs/GITHUB_ACTIONS.md — reusable GitHub quality gate
examples/integrations/github-actions/ — copy-paste CI gate
docs/BENCHMARKS.md — benchmark policy
docs/DEVELOPMENT.md — engineering notes
Detect → fix → verify / 中文版

License

Apache-2.0 — see LICENSE.

If DataSentry helps you catch b

Only the beginning is shown; read the rest in the repo

DataSentry

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Zoo MCP

README from the maker

Try it in 30 seconds

Start with your problem

Why DataSentry exists

What it catches automatically

Safe repair, not blind mutation

propose a repair without changing data

preview the exact effect

apply to a repaired copy; the original is not overwritten

re-scan the repaired copy and detect regressions

inspect or undo the repair

Quality gates for CI

Give AI agents deterministic data-quality tools

Where it fits next to popular data-quality projects

Local-first by design

Data sources

History and drift

Architecture

Reproducible benchmark

Development

Contributing

Documentation

License