Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 19:17 UTC

crystaldba / postgres-mcp

Postgres MCP Pro provides configurable read/write access and performance analysis for you and your AI agents.

PythonMIT★ 3,311 stars⑂ 376 forkssince Mar 2025View on GitHub ↗
KindCommand-line toolMCP serverLibraryhow this is determined

crystaldba/postgres-mcp holds a health index of 56 out of 100, placing it in the Moderate band. It scores highest on Community & Adoption (68/100) and lowest on Security (42/100). It was last updated 31 days ago. 2 contributors account for most of its recent work.

56
overall / 100
Moderate

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.

56
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 54 is calibrated to 56 on the published index scale (record calibration 2026-08-02).

Ownership

Crystal DBAOrganization
40 followers5 public repossince Jan 2024

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIpostgres-mcp0.3.0613,4119489 days ago

Metrics by category

Vitality

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

43Weak · 21% of overall

Development activity

31At Risk
How it's scored
18/36Push recency — last push 31 days ago
2.8/36Commit cadence — 4/52 weeks with commits
10.3/18Commit volume — 13 commits in the last year
0/10OpenSSF Scorecard: Maintained — 1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year13
human_commit_share1
days_since_last_push31
active_weeks_last_year4

Release discipline

61Moderate
How it's scored
27/27Ships releases — 4 releases published
7.2/36Release recency — latest release 489 days ago
27/27Release cadence — a release every ~12.6 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count4
latest_release_tagv0.3.0
releases_from_tagsno
days_since_latest_release489
mean_days_between_releases12.6

Community & Adoption

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

68Good · 17% of overall

Popularity & adoption

84Excellent
How it's scored
57.1/60Stars — 3,311 stars
21.5/25Forks — 376 forks
5.8/15Watchers — 12 watchers
Inputs used
forks376
stars3,311
watchers12
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

50Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

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

56Moderate · 23% of overall

Maintainer resilience (bus factor)

58Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
12.9/22.5Commit distribution — top contributor authored 42% of commits
13.5/13.5Contributor breadth — 10 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor2
contributors_sampled10
top_contributor_share0.425

Issue & PR responsiveness

38Weak
How it's scored
9.7/42Issue resolution — 23% of issues closed
20.7/30PR acceptance — 73/106 decided PRs merged
0/13Newcomer PR acceptance — 0/8 first-time contributors' PRs merged in 30d
7.5/15OpenSSF Scorecard: Code-Review — Found 16/30 approved changesets -- score normalized to 5
Inputs used
merged_prs73
open_issues50
closed_issues15
prs_merged_7d0
prs_decided_7d5
prs_merged_30d0
prs_decided_30d8
issue_closed_ratio0.231
closed_unmerged_prs33
first_time_authors_30d3
first_time_prs_merged_30d0
first_time_prs_decided_30d8

Ownership & stewardship

53Moderate
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
11.6/25Owner reach — 40 followers of crystaldba
11/25Track record — 5 public repos, account ~2 yr old
Inputs used
followers40
owner_typeOrganization
is_verifiedno
owner_logincrystaldba
public_repos5
account_age_days977

Package maintenance

79Good
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
14/35Publish recency — latest publish 489 days ago
20/20Version history — 9 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespostgres-mcp
ecosystemspypi
any_deprecatedno
min_days_since_publish489

Engineering Quality

Are baseline engineering and documentation practices in place?

61Moderate · 19% of overall

Engineering practices

68Good
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black])
0/9.6Pre-commit hooks
0/6.4.editorconfig
4/20OpenSSF Scorecard: CI-Tests — 7 out of 28 merged PRs checked by a CI test -- score normalized to 2
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

42Weak · 16% of overall

Security posture

28At Risk
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
0.5/2.5CI-Tests — 7 out of 28 merged PRs checked by a CI test -- score normalized to 2
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
3.8/7.5Code-Review — Found 16/30 approved changesets -- score normalized to 5
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
0/7.5Maintained — 1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 79 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate2.8
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging. 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_packages63
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:postgres-mcp@0.3.0 runtime dependency closure — what installing the published package pulls in — 63 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.

68Good · 4% of overall

Agent context & guidance

55Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://modelcontextprotocol.io/llms.txt)
40/40Legible commit history — 69 of 80 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://modelcontextprotocol.io/llms.txt
legible_history_share0.863
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

71Good
How it's scored
18/18One-command bootstrap — justfile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black])
0/11Static type checking
10/10Reproducible environment — Dockerfile, lockfile
10/10Demonstrated agent practice — 8 of the last 80 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_filesjustfile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.1
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/45 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes56,446
source_files_sampled45
oversized_source_files0

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalyes
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

3,311GitHub stars
10contributors
13commits, last 12 months
31days since last push
4releases
2bus factor
50open 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 ★ / 376 ⇿
0Stars
376Forks
3Releases

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.

012525037536452025-042025-122026-09
Major 0Minor 2Patch 1

Each point covers 2 days.

OpenSSF Scorecard 2.8 / 10
2.8aggregate

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 · 2026-09-17 19:16 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
2CI-Tests7 out of 28 merged PRs checked by a CI test -- score normalized to 2
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5Code-ReviewFound 16/30 approved changesets -- score normalized to 5
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities79 existing vulnerabilities detected
Direct dependencies 7
RegistryPackageVersion constraintManifest
PyPImcp>=1.25.0,<2.0pyproject.toml
PyPIpsycopg>=3.3.2pyproject.toml
PyPIhumanize>=4.15.0pyproject.toml
PyPIpglast==7.11pyproject.toml
PyPIattrs>=25.4.0pyproject.toml
PyPIpsycopg-pool>=3.3.0pyproject.toml
PyPIinstructor>=1.14.4pyproject.toml
All dependencies 83

Full resolved dependency set from the GitHub dependency graph: 7 direct and 76 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIattrs25.4.0direct
PyPIhumanize4.15.0direct
PyPIinstructor1.14.4direct
PyPImcp1.25.0direct
PyPIpglast7.11direct
PyPIpsycopg3.3.2direct
PyPIpsycopg-pool3.3.0direct
PyPIaiohappyeyeballs2.6.1indirect
PyPIaiohttp3.11.16indirect
PyPIaiosignal1.3.2indirect
PyPIannotated-types0.7.0indirect
PyPIanyio4.9.0indirect
PyPIcertifi2025.1.31indirect
PyPIcffi2.0.0indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.4.1indirect
PyPIclick8.1.8indirect
PyPIcolorama0.4.6indirect
PyPIcryptography46.0.3indirect
PyPIdiskcache5.6.3indirect
PyPIdistlib0.4.0indirect
PyPIdistro1.9.0indirect
PyPIdocker7.1.0indirect
PyPIdocstring-parser0.16indirect
PyPIfilelock3.20.3indirect
PyPIfrozenlist1.6.0indirect
PyPIh110.14.0indirect
PyPIhttpcore1.0.7indirect
PyPIhttpx0.28.1indirect
PyPIhttpx-sse0.4.0indirect
PyPIidentify2.6.16indirect
PyPIidna3.10indirect
PyPIiniconfig2.1.0indirect
PyPIjinja23.1.6indirect
PyPIjiter0.11.1indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPImarkdown-it-py3.0.0indirect
PyPImarkupsafe3.0.2indirect
PyPImdurl0.1.2indirect
PyPImultidict6.4.3indirect
PyPInodeenv1.9.1indirect
PyPIopenai2.15.0indirect
PyPIpackaging24.2indirect
PyPIplatformdirs4.5.1indirect
PyPIpluggy1.5.0indirect
PyPIpostgres-mcp0.3.0indirect
PyPIpre-commit4.5.1indirect
PyPIpropcache0.3.1indirect
PyPIpsycopg-binary3.3.2indirect
PyPIpycparser2.23indirect
PyPIpydantic2.11.1indirect
PyPIpydantic-core2.33.0indirect
PyPIpydantic-settings2.8.1indirect
PyPIpygments2.19.1indirect
PyPIpyjwt2.10.1indirect
PyPIpyright1.1.408indirect
PyPIpytest9.0.2indirect
PyPIpytest-asyncio1.3.0indirect
PyPIpython-dotenv1.1.0indirect
PyPIpython-multipart0.0.21indirect
PyPIpywin32310indirect
PyPIpyyaml6.0.3indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.32.3indirect
PyPIrich13.9.4indirect
PyPIrpds-py0.30.0indirect
PyPIruff0.14.13indirect
PyPIshellingham1.5.4indirect
PyPIsniffio1.3.1indirect
PyPIsse-starlette2.2.1indirect
PyPIstarlette0.46.1indirect
PyPItenacity9.1.2indirect
PyPItqdm4.67.1indirect
PyPIty0.0.12indirect
PyPItyper0.21.1indirect
PyPItyping-extensions4.13.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.2indirect
PyPIurllib32.3.0indirect
PyPIuvicorn0.34.0indirect
PyPIvirtualenv20.36.1indirect
PyPIyarl1.20.0indirect
Dependency advisories 0

Installing pypi:postgres-mcp@0.3.0 pulls in 63 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.

The message is kept through sign-in.

Related records

Inspected repositories sharing catalogue tags or ecosystems with crystaldba/postgres-mcp. Read them side by side →

100run-llama/llama_indexLlamaIndex is the document processing platform for AIExceptional100aws/aws-sam-cliCLI tool to build, test, debug, and deploy Serverless applications using AWS SAMExceptional100awslabs/mcpOpen source MCP Servers for AWSExceptional100psf/blackThe uncompromising Python code formatterExceptional100apache/arrowApache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analyticsExceptional100PrefectHQ/prefectPrefect is a workflow orchestration framework for building resilient data pipelines in Python.Exceptional

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.