YouTube Research MCP
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Alternatives to YouTube Research MCP
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Related Servers
- AlicenseNot gradedqualityNot gradedmaintenanceAn MCP server that provides YouTube data access without API keys or quotas. It enables agents to search videos, retrieve transcripts and metadata, and perform full-text search across cached content for AI context retrieval.3 npm-
- AlicenseAqualityAmaintenanceMCP server that provides YouTube video data to AI agents, supporting search, metadata, comments, and transcripts without an API key.6111 npmISC
- AlicenseAqualityBmaintenanceA YouTube MCP server that enables AI agents to search videos, fetch transcripts, comments, and channel info, with optional API key for extra features and local semantic search via corpora.1741 PyPIMIT
- AlicenseAqualityAmaintenanceMCP server for extracting structured intelligence from YouTube channels and videos — transcripts, topics, and competitive signals for AI-powered research workflows.165 npmMIT
- AlicenseAqualityCmaintenanceA production-ready MCP server for YouTube integration with intelligent caching. Enables searching videos, retrieving transcripts, analyzing channels, and monitoring live streams via natural language.311MIT
- AlicenseNot gradedqualityBmaintenanceA comprehensive MCP server providing YouTube transcript retrieval, video search, channel browsing, playlist extraction, and upload monitoring for AI agents.13MIT
TDQS
Scored across 4 tools
Each tool has a clearly distinct role: search discovers videos, fetch reads specific videos, channel lists a single channel's videos with outlier scoring, and doctor diagnoses connectivity. The only mild overlap is that search and channel both return video lists, but their query-based vs. per-channel framing is spelled out clearly.
All tools share the youtube_ prefix and snake_case, giving a strong predictable pattern. There is a minor deviation in that search/fetch are verbs while channel/doctor are nouns, but the convention is still readable and consistent.
Four tools is well-scoped for a focused research server, and each earns its place: discovery, deep reading, channel analysis, and diagnostics. No tool feels redundant or tacked on.
The surface covers the core research lifecycle: find videos, read transcripts/frames/comments, analyze channels, and self-diagnose failures. Minor gaps remain, such as no dedicated playlist handling or channel/creator search, but agents can work around these.