SpotifyScraper: Extract public Spotify data — tracks, albums, artists, playlists, podcasts & lyrics — without the official API

by AliAkhtari78 · MCP Server · ★ 317

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

How to install SpotifyScraper

pip install spotifyscraper                 # core (only depends on httpx)
pip install "spotifyscraper[media]"        # + cover/preview embedding (mutagen)

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 70/100

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. Full audit →

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.

asyncclaudehttpxllmlyricsmcpmcp-servermetadatamodel-context-protocolmusic

Quick Facts

Stars317
Forks33
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score70/100
Open Issues14
Last Updated2026-09-18
Created2020-04-28
Platformsbrowser, claude-code, mcp, python
Est. Tokens~18k

About SpotifyScraper

SpotifyScraper Extract public Spot

Compatible Skills

These tools work well together with SpotifyScraper for enhanced workflows:

  • personalized-podcast — semantic(0.26)+complementary+rare_topics+same_lang+similar_pop+shared_platform (64%)
  • scrapai-cli — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • llm-reader — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • oxylabs-ai-studio-py — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (58%)
  • mcp-claude-spotify — semantic(0.65)+rare_topics+similar_pop+shared_platform (57%)

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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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