Video & Audio Editing MCP Server

Instructions

Implementation Reference

• server.py:1102-1219 (handler)

  The core handler function for the remove_silence MCP tool. It uses FFmpeg's silencedetect filter to identify silent segments, parses the output to determine non-silent segments, and concatenates those segments using select and setpts filters for video/audio, effectively removing silences. The @mcp.tool() decorator registers this function as an MCP tool named 'remove_silence' with input schema inferred from type annotations and docstring.
  @mcp.tool() def remove_silence(media_path: str, output_media_path: str, silence_threshold_db: float = -30.0, min_silence_duration_ms: int = 500) -> str: """Removes silent segments from an audio or video file. Args: media_path: Path to the input audio or video file. output_media_path: Path to save the media file with silences removed. silence_threshold_db: The noise level (in dBFS) below which is considered silence (e.g., -30.0). min_silence_duration_ms: Minimum duration (in milliseconds) of silence to be removed (e.g., 500). Returns: A status message indicating success or failure. """ if not os.path.exists(media_path): return f"Error: Input media file not found at {media_path}" if min_silence_duration_ms <= 0: return "Error: Minimum silence duration must be positive." min_silence_duration_s = min_silence_duration_ms / 1000.0 try: # Step 1: Detect silence using silencedetect filter # The output of silencedetect is written to stderr silence_detection_process = ( ffmpeg .input(media_path) .filter('silencedetect', n=f'{silence_threshold_db}dB', d=min_silence_duration_s) .output('-', format='null') # Output to null as we only need stderr .run_async(pipe_stderr=True) ) _, stderr_bytes = silence_detection_process.communicate() stderr_str = stderr_bytes.decode('utf8') # Step 2: Parse silencedetect output from stderr silence_starts = [float(x) for x in re.findall(r"silence_start: (\d+\.?\d*)", stderr_str)] silence_ends = [float(x) for x in re.findall(r"silence_end: (\d+\.?\d*)", stderr_str)] # silencedetect might also output silence_duration, but start/end are more direct for segmenting if not silence_starts: # No silences detected, or only one long silence which means the file might be entirely silent or entirely loud # If the file is entirely silent, ffmpeg might not produce silence_start/end, or it might be one large segment. # A robust way to check if any sound exists might be needed if this is problematic. # For now, if no silences are explicitly detected, we can assume no segments need removing. # Or, copy the file as is. # Let's try to copy the file as is, as no silences were detected for removal. try: ffmpeg.input(media_path).output(output_media_path, c='copy').run(capture_stdout=True, capture_stderr=True) return f"No significant silences detected (or file is entirely silent/loud). Original media copied to {output_media_path}." except ffmpeg.Error as e_copy: return f"No significant silences detected, but error copying original file: {e_copy.stderr.decode('utf8') if e_copy.stderr else str(e_copy)}" # Ensure starts and ends are paired correctly. Silencedetect should output them in order. # If there's a mismatch, it indicates a parsing error or unexpected ffmpeg output. if len(silence_starts) != len(silence_ends): # It's possible for a file to end in silence, in which case silence_end might be missing for the last detected silence_start. # Or start with silence, where silence_start is 0. # A more robust parsing might be needed if ffmpeg output varies significantly. # For now, we assume they are paired from the output. If not, it's an issue. pass # Continue and see, this might mean it ends with silence and last end is EOF # Get total duration of the media for the last segment probe = ffmpeg.probe(media_path) duration_str = probe['format']['duration'] total_duration = float(duration_str) # Step 3: Construct segments to keep (non-silent parts) sound_segments = [] current_pos = 0.0 for i in range(len(silence_starts)): start_silence = silence_starts[i] end_silence = silence_ends[i] if i < len(silence_ends) else total_duration if start_silence > current_pos: # There is sound before this silence sound_segments.append((current_pos, start_silence)) current_pos = end_silence # Move current position to the end of this silence if current_pos < total_duration: # There is sound after the last silence detected sound_segments.append((current_pos, total_duration)) if not sound_segments: return f"Error: No sound segments were identified to keep. The media might be entirely silent according to the thresholds, or too short." # Step 4: Construct select filter expressions video_select_filter_parts = [] audio_select_filter_parts = [] for start, end in sound_segments: video_select_filter_parts.append(f'between(t,{start},{end})') audio_select_filter_parts.append(f'between(t,{start},{end})') video_select_expr = "+".join(video_select_filter_parts) audio_select_expr = "+".join(audio_select_filter_parts) # Step 5: Apply filters and output input_media = ffmpeg.input(media_path) has_video = any(s['codec_type'] == 'video' for s in probe['streams']) has_audio = any(s['codec_type'] == 'audio' for s in probe['streams']) output_streams = [] if has_video: processed_video = input_media.video.filter('select', video_select_expr).filter('setpts', 'PTS-STARTPTS') output_streams.append(processed_video) if has_audio: processed_audio = input_media.audio.filter('aselect', audio_select_expr).filter('asetpts', 'PTS-STARTPTS') output_streams.append(processed_audio) if not output_streams: return "Error: The input media does not seem to have video or audio streams." ffmpeg.output(*output_streams, output_media_path).run(capture_stdout=True, capture_stderr=True) return f"Silent segments removed. Output saved to {output_media_path}" except ffmpeg.Error as e: error_message = e.stderr.decode('utf8') if e.stderr else str(e) return f"Error removing silence: {error_message}" except Exception as e: return f"An unexpected error occurred while removing silence: {str(e)}"