Public record
Software health reportschema 0.14.0 · metrics 2.10.0 · 2026-07-19 05:49 UTC

surendranb / google-analytics-mcp

Google Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to website traffic, user behavior, and performance data with schema discovery, server-side aggregation, and safe defaults that reduce data wrangling.

Python · JavaScriptApache-2.0★ 229 stars⑂ 44 forkssince May 2025View on GitHub ↗
KindMCP serverLibraryhow this is determined
#ai-agent#analytics#claude-mcp#ga4#gemini-extension#google-analytics#mcp-server#model-context-protocol

surendranb/google-analytics-mcp holds a health index of 59 out of 100, placing it in the Moderate band. It scores highest on Vitality (81/100) and lowest on AI Readiness (20/100). It was last updated today. A single contributor accounts for most of its recent work.

59
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.

59
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 56 is calibrated to 59 on the published index scale (record calibration 2026-08-02).

Ownership

surendranbPersonal account
17 followers63 public repossince Dec 2012Atlan

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIgoogle-analytics-mcp2.8.129,304510 days agogoogle-analyticsmcpmcp-serverai-agentanalyticsga4agentic-workflowsllm

Metrics by category

Vitality

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

81Excellent · 21% of overall

Development activity

69Good
How it's scored
36/36Push recency — last push 0 days ago
5.5/36Commit cadence — 8/52 weeks with commits
17.5/18Commit volume — 87 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year87
human_commit_share—
days_since_last_push0
active_weeks_last_year8

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 36 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count36
latest_release_tagv2.8.1
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases1
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?

57Moderate · 17% of overall

Popularity & adoption

52Moderate
How it's scored
38.2/60Stars — 229 stars
13.6/25Forks — 44 forks
0/15Watchers — 0 watchers
Inputs used
forks44
stars229
watchers0
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 (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges—
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Ecosystem adoption (downloads)

74Good
How it's scored
59.6/80Monthly downloads — 29,304 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesgoogle-analytics-mcp
dependents—
ecosystemspypi
total_downloads—
monthly_downloads29,304
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?

45Weak · 23% of overall

Maintainer resilience (bus factor)

13Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1

Issue & PR responsiveness

31At Risk
How it's scored
17.5/42Issue resolution — 42% of issues closed
0/30PR acceptance — no decided pull requests or no data
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs0
open_issues7
closed_issues5
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio0.417
closed_unmerged_prs0
first_time_authors_30d—
first_time_prs_merged_30d—
first_time_prs_decided_30d—
Excluded from scoring (no data or not applicable): PR acceptance, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

55Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
9/25Owner reach — 17 followers of surendranb
25/25Track record — 63 public repos, account ~13 yr old
Inputs used
followers17
owner_typeUser
is_verified—
owner_loginsurendranb
public_repos63
account_age_days4,962
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

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 — 51 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesgoogle-analytics-mcp
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

58Moderate · 19% of overall

Engineering practices

30At Risk
How it's scored
24/24CI workflows — 2 workflow(s)
0/24Tests 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_testsno
has_editorconfigno
has_linter_configno
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://ga4mcp.com
10/10Repository description
10/10Topics — 8 topics
10/10Wiki
Inputs used
topicsai-agent, analytics, claude-mcp, ga4, gemini-extension, google-analytics, mcp-server, model-context-protocol
has_wikiyes
homepagehttps://ga4mcp.com
docs_sitehttps://ga4mcp.com
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

44Weak · 16% of overall

Security posture

44Weak
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
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
7.5/7.5Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
1/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
0/5SAST — no SAST tool detected
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): CI-Tests, Signed-Releases. Remaining weights renormalized.

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.

20At Risk · 4% of overall

Agent context & guidance

1Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share—
agent_instruction_files—
agent_instruction_max_bytes—
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

15Critical
How it's scored
0/18One-command bootstrap
0/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no data
0/8Automated maintenance — no data
2/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
Inputs used
has_nixno
has_testsno
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share—
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/15 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes22,135
source_files_sampled15
oversized_source_files0

Machine-readable interfaces

33At Risk
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

229GitHub stars
1contributors
87commits, last 12 months
0days since last push
36releases
1bus factor
7open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Could not fetch npm package 'google-analytics-mcp' from its registry

More detail

OpenSSF Scorecard 4.4 / 10
4.4aggregate

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 · 2026-07-19 05:49 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
2Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 2
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPImcp>=1.2.0pyproject.toml
PyPIgoogle-analytics-data==0.19.0pyproject.toml
PyPIgoogle-auth==2.40.0pyproject.toml
All dependencies 51

Full resolved dependency set from the GitHub dependency graph: 4 direct and 47 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIgoogle-analytics-data0.19.0direct
PyPIgoogle-auth2.40.0direct
PyPImcp—direct
PyPImcp1.28.1direct
PyPIannotated-types0.7.0indirect
PyPIanyio4.14.1indirect
PyPIattrs26.1.0indirect
PyPIcachetools5.5.2indirect
PyPIcertifi2026.6.17indirect
PyPIcffi2.0.0indirect
PyPIcharset-normalizer3.4.7indirect
PyPIclick8.4.2indirect
PyPIcolorama0.4.6indirect
PyPIcryptography49.0.0indirect
PyPIexceptiongroup1.3.1indirect
PyPIgoogle-analytics-mcp2.7.4indirect
PyPIgoogle-api-core2.31.0indirect
PyPIgoogleapis-common-protos1.75.0indirect
PyPIgrpcio1.81.1indirect
PyPIgrpcio-status1.81.1indirect
PyPIh110.16.0indirect
PyPIhttpcore1.0.9indirect
PyPIhttpx0.28.1indirect
PyPIhttpx-sse0.4.3indirect
PyPIidna3.18indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPIproto-plus1.28.0indirect
PyPIprotobuf6.33.6indirect
PyPIpyasn10.6.3indirect
PyPIpyasn1-modules0.4.2indirect
PyPIpycparser3.0indirect
PyPIpydantic2.13.4indirect
PyPIpydantic-core2.46.4indirect
PyPIpydantic-settings2.14.2indirect
PyPIpyjwt2.13.0indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-multipart0.0.32indirect
PyPIpywin32312indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.34.2indirect
PyPIrpds-py0.30.0indirect
PyPIrpds-py2026.5.1indirect
PyPIrsa4.9.1indirect
PyPIsetuptools—indirect
PyPIsse-starlette3.4.5indirect
PyPIstarlette1.3.1indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPIurllib32.7.0indirect
PyPIuvicorn0.49.0indirect
Raw JSON report machine-readable

Feedback

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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 v0.14.0 — full methodology · metrics wiki.

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