DataSentry v1.0.3 — Official MCP Registry discovery

DataSentry v1.0.3 — Official MCP Registry discovery

DataSentry v1.0.3 makes its local-first data-quality MCP server ready for discovery through the official Model Context Protocol Registry.

What's new

• Added canonical MCP Registry metadata under io.github.jackxiaozhiren/datasentry.
• Added the PyPI ownership marker required for Registry verification.
• Added official mcp-publisher validation to pull requests.
• Added automatic MCP Registry publishing after a successful PyPI release.
• Registry authentication uses GitHub OIDC — no long-lived Registry publishing token is required.
• Added release-time validation to keep the application version, Registry version, PyPI package metadata, and ownership marker aligned.
• Documented the MCP Registry distribution and release workflow.

Install

pip install --upgrade datasentry-ai
datasentry demo

MCP

DataSentry provides a local stdio MCP server:

datasentry mcp --project /path/to/project

Its core safety model remains unchanged:

Find → Explain → Fix safely → Verify

Detection and scoring remain deterministic by default. Repairs are previewed, applied to copies, auditable, reversible, and state-changing MCP operations should remain user-approved.

GitHub Actions quality gate

DataSentry also provides a reusable GitHub Actions quality gate:

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

Release validation

This release passed the complete DataSentry CI gate, including:

• Ruff lint and formatting
• mypy strict
• pytest and coverage gate
• zero-config demo smoke test
• 1M-row benchmark
• CLI smoke tests
• API and Web UI smoke tests
• artifact validation
• isolated installation of the exact release wheels
• official MCP Registry metadata validation

Full documentation and examples are available in the repository.