SRT Translation MCP Server

## Context SRT (SubRip Subtitle) files are widely used for video subtitles and require precise timing, formatting, and content management. The challenge is creating an MCP server that can efficiently process large SRT files, maintain timing accuracy, and provide intelligent translation capabilities while preserving subtitle formatting and style tags. ## Goals / Non-Goals ### Goals - Create a robust MCP server for SRT file processing and translation - Handle large SRT files through intelligent chunking and partial editing - Preserve timing synchronization with original files - Detect conversation patterns for better translation context - Maintain SRT formatting and style tags - Provide efficient translation workflows ### Non-Goals - Real-time subtitle generation (focus on file processing) - Video file processing (SRT files only) - Advanced machine learning models (use existing translation APIs) - GUI interface (MCP server for AI assistant integration) ## Decisions ### Decision: Node.js MCP Server Framework - **Why**: Node.js provides excellent file I/O capabilities, streaming support for large files, and strong ecosystem for text processing - **Alternatives considered**: Python (more complex deployment), Go (less ecosystem), Rust (overkill for this use case) ### Decision: Streaming-based SRT Processing - **Why**: Large SRT files can be several MB; streaming prevents memory issues and enables partial processing - **Alternatives considered**: Load entire file (memory issues), database storage (unnecessary complexity) ### Decision: Context-aware Chunking Strategy - **Why**: Break files at natural conversation boundaries rather than arbitrary time intervals for better translation context - **Alternatives considered**: Fixed-size chunks (loses context), time-based chunks (may split conversations) ### Decision: Style Tag Preservation - **Why**: SRT files often contain HTML-like tags for formatting; these must be preserved during translation - **Alternatives considered**: Strip tags (loses formatting), ignore tags (breaks timing) ## Risks / Trade-offs ### Risk: Memory Usage with Large Files - **Mitigation**: Implement streaming processing and configurable chunk sizes ### Risk: Timing Drift During Translation - **Mitigation**: Validate timing after each operation and provide timing correction tools ### Risk: Style Tag Corruption - **Mitigation**: Parse and preserve tags separately from content, validate after translation ### Trade-off: Processing Speed vs. Context Quality - **Decision**: Prioritize context quality for better translations, accept slower processing ## Migration Plan 1. **Phase 1**: Core MCP server with basic SRT parsing 2. **Phase 2**: Add chunking and partial editing capabilities 3. **Phase 3**: Implement conversation detection 4. **Phase 4**: Add translation workflow integration 5. **Phase 5**: Style tag preservation and validation ## Open Questions - Should the server support multiple SRT format variants (WebVTT, ASS)? - How to handle overlapping subtitles during translation? - What's the optimal chunk size for different file sizes? - Should translation be synchronous or support async processing?