Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-14 18:30 UTC

SepineTam / mcp-for-stata

A MCP server for integrating Stata into your agent loop with a safety-first design.

PythonAGPL-3.0★ 259 stars⑂ 36 forkssince Mar 2025View on GitHub ↗
KindMCP serverCommand-line toolLibraryhow this is determined
#mcp#stata#econometrics#llm#statistical-analysis#empirical#empirical-research#social-science#social-science-research#dsh-plugin#dsh-plugins#claude-code#claude-code-plugin#claude-code-skill#codex#codex-plugin#stata-mcp#safety#security

SepineTam/mcp-for-stata holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Vitality (100/100) and lowest on AI Readiness (54/100). It was last updated today. A single contributor accounts for most of its recent work.

86
overall / 100
Excellent

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.

86
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 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

S.TamPersonal account
43 followers112 public repossince Apr 2024Shanghai Bayes View Information Technology Co., Ltd.

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
PyPIstata-mcp1.24.012,8321088 days agostatamcpmodel-context-protocolmcp-serverclaude-codecodexopenclawai-agentagenteconometricscausal-inferencedidregressionempirical-researchdata-analysisdo-fileadocomputational-social-science

Metrics by category

Vitality

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

100Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 873 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year873
human_commit_share0.99
days_since_last_push0
active_weeks_last_year52

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 8 days ago
27/27Release cadence — a release every ~6.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv1.24.0
releases_from_tagsno
days_since_latest_release8
mean_days_between_releases6.8

Community & Adoption

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

62Moderate · 17% of overall

Popularity & adoption

52Moderate
How it's scored
39.1/60Stars — 259 stars
12.9/25Forks — 36 forks
0/15Watchers — 1 watchers
Inputs used
forks36
stars259
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (AGPL-3.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges9
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io, www.bestpractices.dev
has_pull_request_templateno

Ecosystem adoption (downloads)

68Good
How it's scored
54.8/80Monthly downloads — 12,832 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesstata-mcp
dependents—
ecosystemspypi
total_downloads—
monthly_downloads12,832
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?

59Moderate · 23% of overall

Maintainer resilience (bus factor)

18Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.2/22.5Commit distribution — top contributor authored 99% of commits
6.8/13.5Contributor breadth — 5 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled5
top_contributor_share0.99

Issue & PR responsiveness

85Excellent
How it's scored
38.5/42Issue resolution — 92% of issues closed
26.9/30PR acceptance — 70/78 decided PRs merged
6.5/13Newcomer PR acceptance — 1/2 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs70
open_issues2
closed_issues22
prs_merged_7d0
prs_decided_7d1
prs_merged_30d3
prs_decided_30d4
issue_closed_ratio0.917
closed_unmerged_prs8
first_time_authors_30d2
first_time_prs_merged_30d1
first_time_prs_decided_30d2

Ownership & stewardship

50Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
11.8/25Owner reach — 43 followers of SepineTam
17.9/25Track record — 112 public repos, account ~2 yr old
Inputs used
followers43
owner_typeUser
is_verified—
owner_loginSepineTam
public_repos112
account_age_days893
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 8 days ago
20/20Version history — 108 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesstata-mcp
ecosystemspypi
any_deprecatedno
min_days_since_publish8

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall

Engineering practices

60Moderate
How it's scored
24/24CI workflows — 6 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

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://www.aidea-labs.com/open/projects/mcp-for-stata
10/10Repository description
10/10Topics — 19 topics
10/10Wiki
Inputs used
topicsmcp, stata, econometrics, llm, statistical-analysis, empirical, empirical-research, social-science, social-science-research, dsh-plugin, dsh-plugins, claude-code, claude-code-plugin, claude-code-skill, codex, codex-plugin, stata-mcp, safety, security
has_wikiyes
homepagehttps://www.aidea-labs.com/open/projects/mcp-for-stata
docs_sitehttps://www.aidea-labs.com/open/projects/mcp-for-stata
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

64Moderate · 16% of overall

Security posture

55Moderate
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes

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_packages54
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:stata-mcp@1.24.0 runtime dependency closure — what installing the published package pulls in — 54 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.

54Moderate · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — CLAUDE.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 89 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.899
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes6,612

Verify loop (build / test / typecheck)

38Weak
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 1 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0.02
toolchain_manifests—
dependency_bot_commit_share0.01

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/155 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes53,302
source_files_sampled155
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

259GitHub stars
5contributors
873commits, last 12 months
0days since last push
100releases
1bus factor
2open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (exit code 0; Skipping github.com/SepineTam/mcp-for-stata: run: RepoClient.LocalPath: error during tarballHandler.setup: internal error: handler.httpClient.Do: Get "https://codeload.github.com/SepineTam/mcp-for-stata/legacy.tar.gz/refs/heads/master": internal error: innerTransport.RoundTrip: internal error: innerTransport.RoundTrip: error in HTTP: dial tcp: lookup codeload.github.com on 127.0.0.11:53: server misbehaving); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 36 ⇿
0Stars
36Forks
93Releases

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.

01325383522025-072026-022026-09
Major 0Minor 18Patch 75

Each point covers 2 days.

Direct dependencies 12
RegistryPackageVersion constraintManifest
PyPIinstalled-apps-scanner>=0.1.0,<0.2pyproject.toml
PyPIwinapps>=0.2.0,<0.3pyproject.toml
PyPImcp>=2.1.1,<3.0pyproject.toml
PyPIopentelemetry-sdk>=1.44.0,<2.0pyproject.toml
PyPIcryptography>=50.0.0pyproject.toml
PyPIpandas>=3.0.0,<4.0.0pyproject.toml
PyPIpexpect>=4.9.0pyproject.toml
PyPIopenpyxl>=3.1.5pyproject.toml
PyPIpsutil>=7.2.2pyproject.toml
PyPIpyreadstat>=1.3.4pyproject.toml
PyPIrequests>=2.34.0pyproject.toml
PyPItomli-w>=1.2.0pyproject.toml
All dependencies 101

Full resolved dependency set from the GitHub dependency graph: 21 direct and 80 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcryptography—direct
PyPIcryptography49.0.0direct
PyPIinstalled-apps-scanner—direct
PyPImcp—direct
PyPImcp1.27.1direct
PyPIopenpyxl—direct
PyPIopenpyxl3.1.5direct
PyPIopentelemetry-sdk—direct
PyPIpandas—direct
PyPIpandas3.0.3direct
PyPIpexpect—direct
PyPIpexpect4.9.0direct
PyPIpsutil—direct
PyPIpsutil7.2.2direct
PyPIpyreadstat—direct
PyPIpyreadstat1.3.4direct
PyPIrequests—direct
PyPIrequests2.34.0direct
PyPItomli-w—direct
PyPItomli-w1.2.0direct
PyPIwinapps—direct
PyPIannotated-doc0.0.4indirect
PyPIannotated-types0.7.0indirect
PyPIanthropic0.101.0indirect
PyPIanyio4.13.0indirect
PyPIattrs26.1.0indirect
PyPIcertifi2026.4.22indirect
PyPIcffi2.0.0indirect
PyPIcharset-normalizer3.4.7indirect
PyPIclick8.3.3indirect
PyPIcolorama0.4.6indirect
PyPIdistro1.9.0indirect
PyPIdocstring-parser0.18.0indirect
PyPIet-xmlfile2.0.0indirect
PyPIgoogle-auth2.52.0indirect
PyPIgoogle-genai2.0.1indirect
PyPIgriffelib2.0.2indirect
PyPIh110.16.0indirect
PyPIhttpcore1.0.9indirect
PyPIhttpx0.28.1indirect
PyPIhttpx-sse0.4.3indirect
PyPIidna3.15indirect
PyPIiniconfig2.3.0indirect
PyPIjiter0.14.0indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPImarkdown-it-py4.2.0indirect
PyPImcp-ui-server—indirect
PyPImcp-ui-server1.0.0indirect
PyPImdurl0.1.2indirect
PyPInarwhals2.21.0indirect
PyPInuitka—indirect
PyPInuitka4.1.3indirect
PyPInumpy2.4.4indirect
PyPIopenai—indirect
PyPIopenai2.36.0indirect
PyPIopenai-agents—indirect
PyPIopenai-agents0.17.2indirect
PyPIopenai-api-polling—indirect
PyPIopenai-api-polling0.1.5indirect
PyPIpackaging26.2indirect
PyPIpluggy1.6.0indirect
PyPIptyprocess0.7.0indirect
PyPIpyasn1—indirect
PyPIpyasn10.6.3indirect
PyPIpyasn1-modules0.4.2indirect
PyPIpycparser3.0indirect
PyPIpydantic2.13.4indirect
PyPIpydantic-core2.46.4indirect
PyPIpydantic-settings2.14.2indirect
PyPIpygments2.20.0indirect
PyPIpyjwt2.13.0indirect
PyPIpytest—indirect
PyPIpytest9.0.3indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-multipart0.0.32indirect
PyPIpywin32311indirect
PyPIreferencing0.37.0indirect
PyPIregex2026.5.9indirect
PyPIrich15.0.0indirect
PyPIrpds-py0.30.0indirect
PyPIshellingham1.5.4indirect
PyPIsix1.17.0indirect
PyPIsniffio1.3.1indirect
PyPIsse-starlette3.4.3indirect
PyPIstarlette1.3.1indirect
PyPIstata-mcp1.21.0indirect
PyPItenacity9.1.4indirect
PyPItiktoken0.12.0indirect
PyPItqdm4.67.3indirect
PyPItyper0.25.1indirect
PyPItypes-requests2.33.0.20260508indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2026.2indirect
PyPIurllib32.7.0indirect
PyPIuv-build—indirect
PyPIuv-build0.11.28indirect
PyPIuvicorn0.46.0indirect
PyPIwebsockets16.0indirect
Dependency advisories 0

Installing pypi:stata-mcp@1.24.0 pulls in 54 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.