mcp-youtube-transcript
Related Servers
Alternatives to mcp-youtube-transcript
No user-submitted related servers found.
Related Servers
- AlicenseAqualityAmaintenanceMCP server that fetches YouTube video transcripts and optionally summarizes them. Supports multiple transcript formats (text, JSON, SRT, WebVTT), multi-language retrieval, and flexible YouTube URL parsing.637 PyPI6MIT
- FlicenseNot gradedqualityDmaintenanceMCP server providing tools to fetch YouTube video transcripts with metadata, supporting direct YouTube transcripts and audio transcription via multiple backends (whisper, AssemblyAI, OpenAI, Gemini).-
- AlicenseBqualityDmaintenanceAn MCP server designed to fetch transcripts for YouTube videos. It enables AI tools to access video text content for tasks like summarization, analysis, and key takeaway extraction.174MIT
- FlicenseBqualityDmaintenanceAn MCP server that extracts transcripts, metadata, and summaries from YouTube videos across various URL formats including Shorts and standard links. It provides comprehensive video data and insights for analysis within MCP-compatible environments.3-
- AlicenseNot gradedqualityCmaintenanceA comprehensive MCP server providing YouTube transcript retrieval, video search, channel browsing, playlist extraction, and upload monitoring for AI agents.10MIT
- FlicenseNot gradedqualityDmaintenanceThis MCP server fetches and extracts transcripts from YouTube videos, enabling AI language models to access and analyze video content.1-
TDQS
Scored across 2 tools
The two tools have distinct purposes: one lists available subtitle tracks, the other retrieves a transcript in a specified language. There is no overlap in functionality, and the descriptions clarify when to use each.
Both tool names follow a consistent verb_noun pattern: 'list_transcript_languages' and 'get_transcript'. This creates a predictable and readable convention.
Two tools is slightly below the typical 3-15 range, but it is well-scoped for a focused YouTube transcript server. The count feels appropriate rather than thin, covering the core use cases.
The tool surface covers the essential lifecycle: discovering available languages and fetching the transcript. The fallback to English and error messaging that suggests other languages complement the workflow, leaving no obvious gaps.