YouTube Transcript MCP Server

YouTube Transcript API – MCP Server Overview

The YouTube Transcript API addresses a common bottleneck for developers building AI‑driven media applications: the need to reliably retrieve spoken content from YouTube videos. Many existing solutions rely solely on the public transcript feature, which is limited to a handful of languages and may be missing entirely for user‑generated content. This MCP server fills that gap by providing a unified, AI‑friendly interface that automatically selects the best source—either an existing transcript or a Whisper‑powered audio transcription—and returns clean, structured text. The result is a robust pipeline that can be plugged into any Claude or other AI assistant workflow without the need for manual preprocessing.

At its core, the server exposes three primary tools: , , and . The first retrieves the transcript in a specified language, performing automatic detection when no explicit language is supplied. If the requested language is unavailable, the server transparently falls back to Whisper, a state‑of‑the‑art speech recognition model, ensuring that content is never left untranscribed. The second tool forces a Whisper extraction regardless of transcript availability, useful when higher accuracy or custom formatting is required. The third tool allows the assistant to discover relevant videos by keyword, returning video IDs and metadata that can then be fed into the transcript tools.

Key capabilities include multilingual support (English, Vietnamese, and auto‑detection for others), progress reporting for long transcription jobs, and automatic cleanup of temporary files, keeping the server lean and secure. The MCP interface makes these functionalities accessible through a simple JSON payload, enabling seamless integration with Claude’s tool invocation system. Developers can compose complex queries—such as “summarize the key points from the latest interview on AI ethics” or “compare user sentiment across two videos”—by chaining these tools within a single prompt.

Real‑world use cases span content moderation, accessibility services, educational platforms, and media analytics. For instance, a news aggregator can automatically fetch transcripts of breaking‑news videos to generate searchable summaries; an accessibility service can provide subtitles for visually impaired users in multiple languages; and a research lab can batch‑process lecture recordings to extract structured knowledge. Because the server handles both transcript retrieval and fallback transcription, it eliminates the need for separate pipelines or manual intervention.

In summary, the YouTube Transcript MCP server offers a reliable, multilingual, and AI‑ready solution for turning video content into actionable text. Its integration with Claude’s tool ecosystem makes it a powerful asset for developers seeking to enrich conversational agents, build media‑centric applications, or unlock insights from the vast reservoir of YouTube videos.