subtitle-mcp
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Alternatives to subtitle-mcp
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Related Servers
- AlicenseNot gradedqualityAmaintenanceProvides local, offline transcription, keyframe extraction, OCR, and pre-publish review of audio, video, and image files, enabling AI agents to see and hear media without cloud or API keys.52 npmApache 2.0
- AlicenseAqualityCmaintenanceEnables MCP clients to transcribe audio/video files locally, generate SRT subtitles, and burn captions into videos via tool calls, without a cloud API.3MIT
- AlicenseNot gradedqualityBmaintenanceEnables LLM agents to process local videos into timestamped, citable text documents and then query them through tools for listing videos, retrieving transcripts, and fetching specific segments, all fully offline.MIT
- AlicenseAqualityBmaintenanceEnables AI agents to analyze videos by producing timestamped transcripts, keyframe contact sheets, and metadata reports from local files or online URLs, fully offline and without API keys.6MIT
- AlicenseBqualityCmaintenanceEnables AI coding agents to safely inspect, trim, reframe, extract audio from, and burn dynamic subtitles into media files through structured, sandboxed FFmpeg/ffprobe tools with validation and dry-run previews.6MIT
- AlicenseAqualityAmaintenanceEnables AI clients to watch local video files or YouTube/Bilibili and other supported URLs, receiving timestamped transcripts, subtitles, searchable text and keyframe contact sheets. Runs fully offline with local speech recognition and bundled ffmpeg, so no API key or cloud upload is needed.674 PyPI13MIT
TDQS
Scored across 10 tools
Each tool has a clearly distinct purpose: inspection, reading, searching, repair, constant shifting, drift resync, format conversion, merge/split, and the two-step translation workflow. The only potentially overlapping pair, shift and resync, is explicitly differentiated by constant offset versus drift/frame-rate correction, and the descriptions point to each other. search_subtitles and read_cues also target different needs (text matching versus positional/time-based listing).
All names use consistent snake_case, and most are readable verb_noun or verb_object forms such as search_subtitles, fix_common_problems, read_cues, and merge_or_split. However, several tools use single verbs (shift, resync, convert) or a noun-first name (subtitle_info), so the pattern is not uniformly verb_noun.
Ten tools is well within the ideal range for a focused subtitle-processing server. Each tool covers a meaningful, non-redundant operation in the workflow, from inspection and repair through timing adjustment, conversion, merging, and translation.
The surface covers the core subtitle lifecycle: inspect, read, search, repair, shift/resync timing, convert formats, merge/split, and translate text. Minor gaps remain, such as direct cue-level text editing outside the translation flow or explicit cue addition/deletion, but most practical workflows can be handled with the existing tools.