Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-20 03:58 UTC

microsoft / mssql-python

Microsoft Python Driver for SQL Server

Python · C++Custom license★ 479 stars⑂ 62 forkssince Jan 2025View on GitHub ↗
KindLibraryhow this is determined
#microsoft#microsoft-sql-server#python#sql-server

microsoft/mssql-python holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Vitality (99/100) and lowest on Security (62/100). It was last updated today. 2 contributors account for most of its recent work.

96
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

96
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 84 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

MicrosoftOrganization · verified domain
132,176 followers8,381 public repossince Dec 2013

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPImssql-python1.16.02,781,516176 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

99Exceptional · 21% of overall

Development activity

99Exceptional
How it's scored
36/36Push recency — last push 0 days ago
34.6/36Commit cadence — 50/52 weeks with commits
18/18Commit volume — 277 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 10 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year277
human_commit_share1
days_since_last_push0
active_weeks_last_year50

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 32 releases published
36/36Release recency — latest release 6 days ago
27/27Release cadence — a release every ~16.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count32
latest_release_tagv1.16.0
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases16.2
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

81Excellent · 17% of overall

Popularity & adoption

64Moderate
How it's scored
43.5/60Stars — 479 stars
14.9/25Forks — 62 forks
5.8/15Watchers — 12 watchers
Inputs used
forks62
stars479
watchers12
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

86Excellent
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services—
has_pull_request_templateyes

Ecosystem adoption (downloads)

100Exceptional
How it's scored
80/80Monthly downloads — 2,781,516 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmssql-python
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,781,516
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

86Excellent · 23% of overall

Maintainer resilience (bus factor)

64Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.3/22.5Commit distribution — top contributor authored 32% of commits
13.5/13.5Contributor breadth — 22 contributors
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor2
contributors_sampled22
top_contributor_share0.322

Issue & PR responsiveness

87Excellent
How it's scored
35.9/42Issue resolution — 85% of issues closed
25/30PR acceptance — 448/537 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs448
open_issues36
closed_issues211
prs_merged_7d2
prs_decided_7d2
prs_merged_30d27
prs_decided_30d32
issue_closed_ratio0.854
closed_unmerged_prs89
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

100Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 132,176 followers of microsoft
25/25Track record — 8,381 public repos, account ~12 yr old
Inputs used
followers132,176
owner_typeOrganization
is_verifiedyes
owner_loginmicrosoft
public_repos8,381
account_age_days4,696

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 6 days ago
20/20Version history — 17 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmssql-python
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 11 workflow(s)
24/24Tests present
16/16Linter config — .flake8, pyproject.toml ([tool.flake8], [tool.pylint], [tool.black], [tool.autopep8])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

75Good
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://pypi.org/project/mssql-python/
10/10Repository description
10/10Topics — 4 topics
10/10Wiki
Inputs used
topicsmicrosoft, microsoft-sql-server, python, sql-server
has_wikiyes
homepagehttps://pypi.org/project/mssql-python/
docs_sitehttps://pypi.org/project/mssql-python/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

62Moderate · 16% of overall

Security posture

53Moderate
How it's scored
0/7.5Binary-Artifacts — binaries present in source code
0/7.5Branch-Protection — branch protection not enabled on development/release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
0/10Dangerous-Workflow — dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 10 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
0/7.5Vulnerabilities — 22 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.3
Excluded from scoring (no data or not applicable): Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages16
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:mssql-python@1.16.0 runtime dependency closure — what installing the published package pulls in — 16 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

78Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — .github/copilot-instructions.md, .github/instructions/local-formatting.instructions.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files.github/copilot-instructions.md, .github/instructions/local-formatting.instructions.md
agent_instruction_max_bytes8,506

Verify loop (build / test / typecheck)

73Good
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — .flake8, pyproject.toml ([tool.flake8], [tool.pylint], [tool.black], [tool.autopep8])
11/11Static type checking — mssql_python/py.typed
10/10Reproducible environment — devcontainer, Dockerfile
10/10Demonstrated agent practice — 51 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontaineryes
has_linter_configyes
typecheck_configsmssql_python/py.typed
agent_commit_share0.51
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

78Good
How it's scored
27/45Type-checkable code — Python with type-check config (mssql_python/py.typed)
51.2/55Manageable file sizes — 11/159 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes657,915
source_files_sampled159
oversized_source_files11

Key facts

479GitHub stars
22contributors
277commits, last 12 months
0days since last push
32releases
2bus factor
36open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 62 ⇿
0Stars
62Forks
30Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

013253850636132025-032025-122026-10
Major 1Minor 15Patch 1

Each point covers 2 days.

OpenSSF Scorecard 5.3 / 10
5.3aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-10-20 03:57 UTC

0Binary-Artifactsbinaries present in source code
0Branch-Protectionbranch protection not enabled on development/release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
0Dangerous-Workflowdangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 10 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
0Vulnerabilities22 existing vulnerabilities detected
All dependencies 138

Full resolved dependency set from the GitHub dependency graph: 0 direct and 138 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIanaconda-auth0.15.2indirect
PyPIanaconda-cli-base0.9.1indirect
PyPIanaconda-client1.14.1indirect
PyPIannotated-doc0.0.5indirect
PyPIannotated-types0.8.0indirect
PyPIanyio4.15.0indirect
PyPIast-serialize0.9.0indirect
PyPIastroid4.0.4indirect
PyPIattrs26.1.0indirect
PyPIautopep8—indirect
PyPIautopep82.3.2indirect
PyPIazure-core1.41.0indirect
PyPIazure-identity—indirect
PyPIazure-identity1.25.3indirect
PyPIbackports-tarfile1.2.0indirect
PyPIbackports-zstd1.7.0indirect
PyPIblack—indirect
PyPIblack26.5.1indirect
PyPIbuild1.6.0indirect
PyPIcertifi2026.7.22indirect
PyPIcffi2.1.1indirect
PyPIcharset-normalizer3.5.1indirect
PyPIclick8.5.0indirect
PyPIcmake4.4.3indirect
PyPIcolorama0.4.6indirect
PyPIconda-package-handling2.6.0indirect
PyPIconda-package-streaming0.13.0indirect
PyPIcoverage—indirect
PyPIcoverage7.16.0indirect
PyPIcpplint—indirect
PyPIcpplint2.0.2indirect
PyPIcryptography48.0.1indirect
PyPIcryptography50.0.1indirect
PyPIdefusedxml0.7.1indirect
PyPIdill0.4.1indirect
PyPIdocutils0.23indirect
PyPIfastjsonschema2.22.2indirect
PyPIflake8—indirect
PyPIflake87.3.0indirect
PyPIh110.16.0indirect
PyPIhttpcore1.0.9indirect
PyPIhttpx0.28.1indirect
PyPIid1.6.1indirect
PyPIidna3.19indirect
PyPIimportlib-metadata9.0.1indirect
PyPIiniconfig2.3.0indirect
PyPIisort9.0.1indirect
PyPIjaraco-classes3.4.0indirect
PyPIjaraco-context6.1.2indirect
PyPIjaraco-functools4.6.0indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPIjupyter-core5.9.1indirect
PyPIkeyring25.7.0indirect
PyPIlibrt0.15.0indirect
PyPIlxml6.1.3indirect
PyPImarkdown-it-py4.2.0indirect
PyPImccabe0.7.0indirect
PyPImdurl0.1.2indirect
PyPImore-itertools11.1.0indirect
PyPImsal1.38.0indirect
PyPImsal-extensions1.3.1indirect
PyPImssql-python-odbc18.6.2indirect
PyPImypy—indirect
PyPImypy2.3.1indirect
PyPImypy-extensions1.1.0indirect
PyPInbformat5.11.1indirect
PyPInh30.3.7indirect
PyPIpackaging26.3indirect
PyPIpathspec1.1.1indirect
PyPIpip26.2.1indirect
PyPIpkce1.0.3indirect
PyPIplatformdirs4.11.7indirect
PyPIpluggy1.6.0indirect
PyPIpolars—indirect
PyPIpolars1.44.1indirect
PyPIpolars-runtime-321.44.1indirect
PyPIpre-commit4.5.1indirect
PyPIpsutil—indirect
PyPIpsutil7.2.2indirect
PyPIpyarrow25.0.1indirect
PyPIpybind11—indirect
PyPIpybind113.1.0indirect
PyPIpycodestyle2.14.0indirect
PyPIpycparser3.0indirect
PyPIpydantic2.13.5indirect
PyPIpydantic-core2.46.5indirect
PyPIpydantic-settings2.15.0indirect
PyPIpyflakes3.4.0indirect
PyPIpygments2.21.0indirect
PyPIpyjwt2.13.0indirect
PyPIpylint—indirect
PyPIpylint4.0.8indirect
PyPIpyodbc5.3.0indirect
PyPIpyproject-hooks1.2.0indirect
PyPIpytest—indirect
PyPIpytest9.1.1indirect
PyPIpytest-asyncio—indirect
PyPIpytest-asyncio1.4.0indirect
PyPIpytest-cov—indirect
PyPIpytest-cov7.1.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-dotenv1.2.3indirect
PyPIpytokens0.4.1indirect
PyPIpytz2026.3.post1indirect
PyPIpywin32-ctypes0.2.3indirect
PyPIpyyaml6.0.3indirect
PyPIreadchar4.2.2indirect
PyPIreadme-renderer46.0indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.34.2indirect
PyPIrequests-toolbelt1.0.0indirect
PyPIrfc39862.0.0indirect
PyPIrich15.0.0indirect
PyPIrpds-py2026.6.3indirect
PyPIsemver3.0.4indirect
PyPIsetuptools—indirect
PyPIsetuptools84.0.0indirect
PyPIshellingham1.5.4indirect
PyPIsix1.17.0indirect
PyPItomli2.4.1indirect
PyPItomlkit0.15.1indirect
PyPItqdm4.70.0indirect
PyPItraitlets5.16.1indirect
PyPItwine7.0.0indirect
PyPItyper0.27.2indirect
PyPItypes-setuptools—indirect
PyPItypes-setuptools84.0.0.20260812indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.4indirect
PyPIunittest-xml-reporting—indirect
PyPIunittest-xml-reporting4.0.0indirect
PyPIurllib32.7.0indirect
PyPIwheel0.48.0indirect
PyPIzipp4.1.0indirect
PyPIzstandard—indirect
PyPIzstandard0.23.0indirect
PyPIzstandard0.25.0indirect
Dependency advisories 0

Installing pypi:mssql-python@1.16.0 pulls in 16 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.