alfanous: Arabic search engine API provides the simple and advanced search in Quran , more features and many interfaces

by Alfanous-team · MCP Server · ★ 289

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install alfanous

pip install alfanous3
pip install alfanous3-mcp

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 69/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

apiarabicindexingmcpmcp-serverpythonquransearch-engine

Quick Facts

Stars289
Forks90
LanguagePython
CategoryMCP Server
LicenseLGPL-3.0
Quality Score69/100
Open Issues23
Last Updated2026-06-14
Created2012-06-10
Platformsmcp, python
Est. Tokens~18k

About alfanous

Alfanous API Alfanous is a Quranic search engine API that provides simple and advanced search capabilities for the Holy Qur'an. It enables developers to build applications that search through Quranic text in Arabic, with support for Buckwalter transliteration, advanced query syntax, and rich metadata.

Compatible Skills

These tools work well together with alfanous for enhanced workflows:

  • relevanceai — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • Flamehaven-Filesearch — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (52%)
  • DiscovAI-search — semantic(0.44)+complementary+similar_pop (45%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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