moreel
Related Servers
Alternatives to moreel
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables agents to research and analyze public short-form video content on Instagram Reels, TikTok, and YouTube Shorts for trend spotting, creator research, competitive briefs, and content ideation.1MIT
- AlicenseAqualityCmaintenanceEnables AI agents to extract TikTok video and soundtrack media, metadata, covers, and manifests from public URLs or IDs through verified downloads and schema access via MCP tools.4MIT
- 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.-
- AlicenseNot gradedqualityCmaintenanceViral-content intelligence for AI agents — 7 read-only MCP tools for TikTok/YouTube hook scoring, virality prediction, trend analysis, and viral template search, with evidence-layer scoring.MIT
- AlicenseNot gradedqualityFmaintenanceGives MCP clients access to YouTube video transcripts and metadata. It lets AI agents read and summarize video content from a URL.3 npmMIT
- AlicenseAqualityBmaintenanceEnables agents to turn public video links into timestamped transcripts, chapters, and clip suggestions for YouTube, Twitch, Kick, and TikTok.3MIT
TDQS
Scored across 8 tools
Each tool has a clearly distinct role: transcribe_video for audio-only transcription, understand_video for full multimodal processing, and the remaining tools operate on the returned video_id for search, evidence, timeline, or semantic mapping. Even the overlapping transcribe/understand pair is explicitly differentiated by use case, and search_video vs find_moment are separated by exhaustive vs. single-best results.
All tool names follow a consistent snake_case verb_noun pattern (transcribe_video, understand_video, search_video, find_moment, get_video_timeline, get_video_map, get_video_entity, get_video_evidence). The pattern is uniform and predictable, making it easy for an agent to infer function from the name alone.
Eight tools is well within the ideal 3–15 range and feels well-scoped for a video understanding server. Each tool has a specific place in the pipeline (ingest, search, timeline, semantic map, evidence lookup) without redundancy or bloat.
The tool surface covers the full workflow: ingest a video (transcribe_video/understand_video), explore it (search_video, find_moment, get_video_timeline), and dig into semantic relationships (get_video_map, get_video_entity, get_video_evidence). The design anticipates the questions agents will ask and provides no dead ends—every returned video_id can be used by downstream tools.