YouTube Transcript Server

The Kimtaeyoon83 Mcp Server YouTube Transcript solves a common bottleneck for AI developers: obtaining accurate, language‑specific captions from YouTube videos without having to manually scrape or use external APIs. By exposing a single, well‑defined tool——the server lets AI assistants like Claude fetch transcripts directly from a video URL or ID, returning the text along with useful metadata such as timestamps and language code. This eliminates the need for custom scraping scripts, reduces latency, and ensures compliance with YouTube’s terms of service.

For developers integrating AI into content‑heavy workflows, the server offers immediate value. It enables chatbots to answer questions about a video’s content, generate summaries or translations, and power search engines that index video text. Because the tool accepts a language parameter, multilingual applications can retrieve Korean subtitles () or English captions (), and the server gracefully handles cases where a requested language is unavailable, providing clear error messages. The inclusion of detailed metadata in responses also allows downstream tools to reconstruct the original timing information, which is essential for applications like subtitle editors or timed question answering.

Key capabilities of the server include:

• Universal URL support: Handles standard YouTube links, shortened URLs, and raw video IDs.
• Language‑specific retrieval: Requests a particular language code; defaults to English if omitted.
• Robust error handling: Detects invalid URLs, missing transcripts, network failures, and provides descriptive feedback.
• Metadata enrichment: Returns timestamps, speaker labels (if available), and other contextual data to aid further processing.

Real‑world use cases span educational platforms that auto‑generate lesson notes from lecture videos, media monitoring services that track brand mentions across YouTube content, and accessibility tools that convert video speech into readable text. In an AI workflow, a developer can simply invoke from within a prompt or a custom tool chain; the assistant then receives structured transcript data ready for summarization, sentiment analysis, or translation—all without leaving the MCP ecosystem.

What sets this server apart is its focus on simplicity and reliability. By abstracting away the complexities of YouTube’s caption APIs, it offers a single point of integration that is both easy to deploy (via Smithery or mcp‑get) and straightforward to extend. Its open‑source MIT license encourages community contributions, while built‑in security checks protect against malformed inputs and API abuse. For any project that needs to bridge the gap between video content and AI understanding, this MCP server provides a clean, dependable solution.