mcp-video-analyzer
Related Servers
Alternatives to mcp-video-analyzer
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityAmaintenanceMCP server that turns video URLs into transcripts and important keyframes locally, with no cloud or API keys.134 npm4MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server for comprehensive video analysis — AI-powered transcription, visual frame analysis, and metadata extraction from 1000+ platforms.1MIT
- AlicenseAqualityAmaintenanceAn MCP server enabling Claude Code to analyze any video (local file, URL, or Jira ticket attachment) by extracting frame images and audio transcripts, or using Gemini for native video analysis.484 PyPIMIT
- AlicenseBqualityFmaintenanceMCP (Model Context Protocol) server that utilizes the Google Gemini Vision API to interact with YouTube videos. It allows users to get descriptions, summaries, answers to questions, and extract key moments from YouTube videos.410 npm6MIT
- FlicenseNot gradedqualityCmaintenanceExtracts transcripts, titles, descriptions, and comments from Loom videos via a remote MCP server.-
- AlicenseAqualityDmaintenanceAn MCP server that enables AI agents to analyze videos locally by extracting transcripts, detecting scene changes, and returning key frames.56MIT
TDQS
Scored across 8 tools
Most tools are clearly distinct: analyze_video is the full pipeline, get_metadata/get_transcript/get_frames are single-aspect extractions, and get_frame_at/get_frame_burst/analyze_moment handle specific temporal queries. The only mild overlap is between analyze_video and analyze_videos (batch vs. single), but the descriptions explicitly differentiate them.
Tool names follow a consistent verb_noun pattern: analyze_videos, analyze_video, analyze_moment, get_metadata, get_frame_at, get_frame_burst, get_frames, get_transcript. The only minor inconsistency is that analyze_moment is a verb_noun pair while the others are verb_noun with the noun being a direct object; still, the pattern is predictable and readable.
8 tools is well-scoped for a video analysis server. Each tool covers a distinct need: full analysis, batch analysis, metadata-only, transcript-only, frames-only, single frame, frame burst, and segment deep-dive. No redundant or filler tools.
The tool surface covers the core video analysis lifecycle well: full analysis, metadata, transcript, frames, and time-range deep dives. Minor gaps include no explicit tool for listing supported platforms or managing cache/sidecars, but these are workarounds and not core analysis operations.