DataSentry MCP server

<!-- mcp-name: io.github.Jackxiaozhiren/datasentry -->

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

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

Shortened. The full README is on GitHub.