mcp-research-pipeline
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- FlicenseNot gradedqualityAmaintenanceEnables MCP clients such as Claude and Copilot to turn YouTube videos into a persistent, compounding wiki by extracting knowledge from transcripts and scene-change vision analysis. Lets clients run agentic search over that compiled knowledge, answering questions with source citations and awareness of what the wiki does not yet cover.-
- FlicenseNot gradedqualityAmaintenanceEnables users to turn YouTube videos into a compounding, searchable knowledge base by combining transcripts with vision analysis and agentic retrieval, while serving that capability to MCP clients like Claude and Copilot.-
- AlicenseNot gradedqualityBmaintenanceEnables Claude Desktop to fetch full YouTube video transcripts from any URL, so users can summarize, translate, or cite video content without API keys or downloads.MIT
- FlicenseNot gradedqualityAmaintenanceEnables ingesting YouTube videos into a persistent, searchable knowledge base by combining transcripts with scene-change vision analysis, then answering natural-language queries with citations. It supports MCP clients such as Claude and Copilot by exposing compiled cross-video knowledge for agentic reasoning.-
- FlicenseBqualityCmaintenanceExtracts YouTube transcripts and performs AI-powered video analysis for Claude Desktop, enabling transcript retrieval, quality analysis, and smart resource management.8-
- FlicenseNot gradedqualityAmaintenanceTurns YouTube videos into a persistent, cross-referenced wiki by extracting transcripts and describing scene-change frames with vision models. Enables MCP clients like Claude and Copilot to search and reason over this compounding knowledge base without needing an API key.-
TDQS
Scored across 15 tools
Tools are mostly distinct by resource and action, with clear separation between YouTube retrieval and NotebookLM management. The main ambiguity is between get_channel_latest and get_channel_videos, though descriptions clarify the convenience vs. paginated distinction. research_topic overlaps with the individual steps but is clearly positioned as an orchestrator.
All tool names use snake_case and generally follow a verb_noun pattern, which keeps the set predictable. Minor inconsistencies like get_channel_latest omitting a noun and mixing list_/get_ for similar operations prevent a perfect score.
At 15 tools, the server sits at the upper bound of a well-scoped set, but the count is justified by covering two integrated domains: YouTube retrieval and NotebookLM management. research_topic and get_channel_latest add some redundancy but still serve useful convenience purposes.
The core research workflow is covered end-to-end: search YouTube, create a notebook, add sources, ask questions, and generate/download artifacts. Missing delete/update operations for notebooks, sources, and artifacts are notable gaps but not critical for the stated pipeline purpose.