Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 22:07 UTC

surendranb / wikipedia-mcp-server

Progressive-retrieval MCP server for Wikipedia

PythonMIT★ 0 stars⑂ 0 forkssince Apr 2026View on GitHub ↗
KindLibraryCommand-line toolMCP serverhow this is determined

surendranb/wikipedia-mcp-server holds a health index of 63 out of 100, placing it in the Moderate band. It scores highest on Vitality (75/100) and lowest on Community & Adoption (39/100). It was last updated 25 days ago. A single contributor accounts for most of its recent work.

63
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.

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

Ownership

surendranbPersonal account
23 followers86 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
PyPImcp-server-wikipedia0.5.22,6251928 days agomcpmcp-servermodel-context-protocolwikipediaragretrievalclaudecursorai-agents
npmmcp-server-wikipedia0.5.2442428 days ago—

Metrics by category

Vitality

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

75Good · 21% of overall
How it's scored
28.8/36Push recency — last push 25 days ago
4.2/36Commit cadence — 6/52 weeks with commits
16/18Commit volume — 60 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year60
human_commit_share1
days_since_last_push25
active_weeks_last_year6

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 6 releases published
36/36Release recency — latest release 28 days ago
27/27Release cadence — a release every ~1.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count6
latest_release_tagv0.5.2
releases_from_tagsno
days_since_latest_release28
mean_days_between_releases1.8
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?

39Weak · 17% of overall
How it's scored
0/60Stars — 0 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars0
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
46.5/80Monthly downloads — 3,067 downloads/month across npm, pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmcp-server-wikipedia, mcp-server-wikipedia
dependents—
ecosystemsnpm, pypi
total_downloads—
monthly_downloads3,067
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?

52Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
1.2/22.5Commit distribution — top contributor authored 95% of commits
2.7/13.5Contributor breadth — 2 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.947
How it's scored
42/42Issue resolution — 100% of issues closed
5/30PR acceptance — 1/6 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/29 approved changesets -- score normalized to 0
Inputs used
merged_prs1
open_issues0
closed_issues2
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio1
closed_unmerged_prs5
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.
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
9.9/25Owner reach — 23 followers of surendranb
25/25Track record — 86 public repos, account ~13 yr old
Inputs used
followers23
owner_typeUser
is_verified—
owner_loginsurendranb
public_repos86
account_age_days5,021
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 2 package(s) on npm, pypi
35/35Publish recency — latest publish 28 days ago
20/20Version history — 19 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmcp-server-wikipedia, mcp-server-wikipedia
ecosystemsnpm, pypi
any_deprecatedno
min_days_since_publish28

Engineering Quality

Are baseline engineering and documentation practices in place?

59Moderate · 19% of overall
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 1 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://wikipedia.builditwithai.xyz
10/10Repository description
10/10Topics — 9 topics
10/10Wiki
Inputs used
topicsai-agents, claude, cursor, knowledge-retrieval, mcp, mcp-server, model-context-protocol, rag, wikipedia
has_wikiyes
homepage—
docs_sitehttps://wikipedia.builditwithai.xyz
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

69Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
0/2.5CI-Tests — 0 out of 1 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/29 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
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 2 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
3.5/5SAST — SAST tool detected but not run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
4.5/7.5Vulnerabilities — 4 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.1
Excluded from scoring (no data or not applicable): Branch-Protection, 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_packages32
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:mcp-server-wikipedia@0.5.2 runtime dependency closure — what installing the published package pulls in — 32 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.

67Good · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt present
40/40Legible commit history — 47 of 60 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_url—
legible_history_share0.783
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes2,341
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 15 of the last 60 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
2/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0.25
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/10 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes34,492
source_files_sampled10
oversized_source_files0
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — openapi.yaml
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
has_mcp_signalyes
api_schema_filesopenapi.yaml
interfaces_expected_of—

Key facts

0GitHub stars
2contributors
60commits, last 12 months
25days since last push
6releases
1bus factor
0open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • pypi download statistics for 'mcp-server-wikipedia' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • Could not fetch npm package 'wikipedia-mcp-website' from its registry
  • pypi download figures for mcp-server-wikipedia carried forward from the previous scan (stats endpoint unavailable this scan)
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

More detail

OpenSSF Scorecard 6.1 / 10
6.1aggregate

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 · 2026-09-15 22:07 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
0CI-Tests0 out of 1 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/29 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 2 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
7SASTSAST tool detected but not run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
6Vulnerabilities4 existing vulnerabilities detected
Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPImcp>=2.0.0,<3pyproject.toml
PyPIrequests>=2.31.0pyproject.toml
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

Dependency advisories 0

Installing pypi:mcp-server-wikipedia@0.5.2 pulls in 32 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.

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, npm.