SRT Translation MCP Server
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Alternatives to SRT Translation MCP Server
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Related Servers
- AlicenseNot gradedqualityDmaintenanceEnables translation of SRT subtitle files from English to Japanese using local LLM servers like LM Studio. Parses SRT format files and returns translated subtitles in proper SRT format through OpenAI-compatible APIs.MIT
- AlicenseNot gradedqualityCmaintenanceEnables local transcription of audio/video files and YouTube URLs, generation of SRT/VTT subtitles, and analysis of speech pacing and audience retention risk using faster-whisper.MIT
- AlicenseBqualityDmaintenanceProvides intelligent transcript processing capabilities for Claude, featuring natural formatting, contextual repair, and smart summarization powered by Deep Thinking LLMs.420MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI to transcribe Japanese talk videos, edit subtitles with filler removal and timing correction, and export SRT files using local Whisper models.MIT
- AlicenseAqualityBmaintenanceEnables AI assistants to transcribe audio and video from URLs or local files with high accuracy, speaker diarization, 119 languages, and word-level timestamps, while also supporting transcription management and caption export in SRT, WebVTT, or plain text.14264 npm11MIT
- AlicenseNot gradedqualityDmaintenanceEnables intelligent transcription of YouTube videos with automatic optimization for any video length, using local OpenAI Whisper processing and speaker diarization.-
TDQS
Scored across 6 tools
Each tool has a distinct, non-overlapping purpose in the SRT translation workflow: detect_conversations analyzes and chunks files, get_next_chunk retrieves chunks sequentially, parse_srt parses SRT content, todo_management manages tasks, translate_srt prepares content for AI translation, and write_srt writes output. The descriptions clearly differentiate their roles, with no ambiguity or overlap in functionality.
Most tools follow a clear verb_noun pattern (e.g., detect_conversations, get_next_chunk, parse_srt, write_srt), which is consistent and predictable. However, translate_srt and todo_management deviate slightly by using a verb_noun format but with less precise action verbs, and todo_management is more generic. Overall, the naming is highly consistent with only minor deviations.
With 6 tools, the server is well-scoped for its purpose of SRT translation. Each tool serves a specific role in the workflow (analysis, chunking, parsing, task management, translation preparation, and output), and none feel redundant or unnecessary. This count is ideal for covering the domain without being overwhelming or insufficient.
The tool set provides complete coverage for the SRT translation domain, supporting a full workflow from input analysis to output generation. It includes detection, chunking, parsing, task management, translation preparation, and file writing, with no obvious gaps. The descriptions emphasize a cohesive process, ensuring agents can handle all necessary operations without dead ends.