What is YouTube2Text?

40/100
Trust Score (E)
🔴 Avoid

YouTube2Text is a AI tool that Extracts clean YouTube video transcripts by removing timecodes and metadata, providing text suitable for content analysis, summarization, and generation workflows through REST API and direct integrati. It has a Nerq Trust Score of 40/100 (E). 0 GitHub stars. Published by https://youtube2text.org. Last analyzed November 2026.

Why This Score

Trust & Safety Overview

40
TRUST SCORE
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GRADE
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STARS
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DOWNLOADS

What YouTube2Text Does

YouTube2Text is a mcp_server in the AI tool category. Extracts clean YouTube video transcripts by removing timecodes and metadata, providing text suitable for content analysis, summarization, and generation workflows through REST API and direct integration.. It is published by https://youtube2text.org and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.

Who Should Use YouTube2Text

YouTube2Text is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.

Details

Authorhttps://youtube2text.org
CategoryAI tool
LicenseNot specified
Typemcp_server
SourceView on GitHub
Security Score0/100
Activity Score0/100

How to Get Started

Check the trust score before installing:

curl nerq.ai/v1/preflight?target=youtube2text

Setup guide · Full safety report · Production review · Is it safe?

Safer Alternatives

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Frequently Asked Questions

What is YouTube2Text used for?
YouTube2Text is a AI tool tool. Extracts clean YouTube video transcripts by removing timecodes and metadata, providing text suitable for content analysis, summarization, and generation workflows through REST API and direct integrati.
Is YouTube2Text free?
License: Check project page. YouTube2Text has 0 GitHub stars.
Is YouTube2Text safe?
YouTube2Text has a Nerq Trust Score of 40/100 (E). Evaluate carefully.
What are alternatives to YouTube2Text?
Top alternatives: MarkItDown, Time, Sequential Thinking. See full comparison.

Last updated November 2026. Trust scores based on automated analysis of public data.

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