Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-22 14:12 UTC

googleanalytics / google-analytics-mcp

PythonApache-2.0★ 3,533 stars⑂ 740 forkssince Jul 2025View on GitHub ↗
KindCommand-line toolMCP serverLibraryhow this is determined

googleanalytics/google-analytics-mcp holds a health index of 71 out of 100, placing it in the Good band. It scores highest on Vitality (81/100) and lowest on Security (21/100). It was last updated today. 2 contributors account for most of its recent work.

71
overall / 100
Good

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.

71
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 63 is calibrated to 71 on the published index scale (record calibration 2026-08-02).

Ownership

Google AnalyticsOrganization
2,136 followers36 public repossince May 2013

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIanalytics-mcp0.8.061,75590 days agogoogle-analyticsanalyticsmcpga4

Metrics by category

Vitality

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

81Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
14.5/36Commit cadence — 21/52 weeks with commits
14.8/18Commit volume — 43 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year43
human_commit_share1
days_since_last_push0
active_weeks_last_year21
How it's scored
27/27Ships releases — 9 releases published
36/36Release recency — latest release 0 days ago
19.8/27Release cadence — a release every ~53.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count9
latest_release_tag0.8.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases53.2

Community & Adoption

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

81Excellent · 17% of overall
How it's scored
57.6/60Stars — 3,533 stars
23.9/25Forks — 740 forks
10.9/15Watchers — 91 watchers
Inputs used
forks740
stars3,533
watchers91
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges7
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno
How it's scored
63.9/80Monthly downloads — 61,755 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesanalytics-mcp
dependents—
ecosystemspypi
total_downloads—
monthly_downloads61,755
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?

77Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
12.8/22.5Commit distribution — top contributor authored 43% of commits
12.2/13.5Contributor breadth — 9 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled9
top_contributor_share0.432
How it's scored
39.4/42Issue resolution — 94% of issues closed
18.4/30PR acceptance — 73/119 decided PRs merged
13/13Newcomer PR acceptance — 2/2 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs73
open_issues5
closed_issues77
prs_merged_7d3
prs_decided_7d4
prs_merged_30d4
prs_decided_30d6
issue_closed_ratio0.939
closed_unmerged_prs46
first_time_authors_30d1
first_time_prs_merged_30d2
first_time_prs_decided_30d2
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
23.9/25Owner reach — 2,136 followers of googleanalytics
23.4/25Track record — 36 public repos, account ~13 yr old
Inputs used
followers2,136
owner_typeOrganization
is_verifiedno
owner_logingoogleanalytics
public_repos36
account_age_days4,920

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 9 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesanalytics-mcp
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

48Weak · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

30At Risk
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
0/10Repository description
0/10Topics
0/10Wiki
Inputs used
topics—
has_wikino
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionno

Security

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

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages2
unassessed_packages6
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 2 resolved dependencies against OSV. 6 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

47Weak · 4% of overall
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 — 72 of 75 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://modelcontextprotocol.io/llms.txt
legible_history_share0.96
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — noxfile.py
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 75
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesnoxfile.py
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/18 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes17,530
source_files_sampled18
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
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

3,533GitHub stars
9contributors
43commits, last 12 months
0days since last push
9releases
2bus factor
5open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:analytics-mcp@0.8.0; advisories assessed against the repository dependency graph instead
  • OpenSSF Scorecard did not return a usable result (exit code 0; Skipping github.com/googleanalytics/google-analytics-mcp: run: internal error: ListCommits:error during graphqlHandler.setup: internal error: githubv4.Query: Something went wrong while executing your query on 2026-10-22T14:12:15Z. Please include `CF3C:36E455:7BFE0EA:B463B8A:6ADA19BC` when reporting this issue.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 740 ⇿
0Stars
740Forks
8Releases

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.

0125250375500625750730152025-072026-032026-10
Major 0Minor 7Patch 1

Each point covers 2 days.

Direct dependencies 6
RegistryPackageVersion constraintManifest
PyPIgoogle-analytics-data==0.23.3pyproject.toml
PyPIgoogle-analytics-admin==0.30.2pyproject.toml
PyPIgoogle-auth~=2.62pyproject.toml
PyPImcp>=2.3.0,<3pyproject.toml
PyPIgoogle-adk>=2.11.0pyproject.toml
PyPIhttpx>=0.28.1pyproject.toml
All dependencies 8

Full resolved dependency set from the GitHub dependency graph: 6 direct and 2 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIgoogle-adk—direct
PyPIgoogle-analytics-admin0.30.1direct
PyPIgoogle-analytics-data0.23.2direct
PyPIgoogle-auth—direct
PyPIhttpx—direct
PyPImcp—direct
PyPInox—indirect
PyPIsetuptools—indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 2 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 6 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

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.

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.