{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-videodb","slug":"videodb","name":"VideoDB","type":"mcp","url":"https://github.com/video-db/agent-toolkit/tree/main/modelcontextprotocol","page_url":"https://unfragile.ai/videodb","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-videodb__cap_0","uri":"capability://search.retrieval.semantic.video.search.with.multimodal.indexing","name":"semantic-video-search-with-multimodal-indexing","description":"Enables searching video content by semantic meaning across visual frames, audio transcripts, and metadata using embeddings-based indexing. The system processes video frames and audio streams through multimodal encoders, stores embeddings in a vector database, and retrieves relevant segments via similarity search. This allows developers to query videos with natural language like 'find scenes with people laughing' without manual tagging.","intents":["Search through hours of video footage by semantic content without manual annotation","Build video discovery features that understand context and meaning, not just keywords","Index and retrieve specific moments across a video library based on visual or audio characteristics","Enable content creators to find usable clips from raw footage by describing what they need"],"best_for":["Video editing platforms and DAWs integrating AI-powered search","Content management systems handling large video libraries","Developers building video discovery or recommendation features","Teams automating video asset management workflows"],"limitations":["Indexing latency scales with video duration and frame sampling rate; full HD video indexing may take minutes per hour of content","Semantic search accuracy depends on quality of underlying multimodal encoder; domain-specific content may require fine-tuned models","Vector database storage requirements grow linearly with video library size and embedding dimensionality","Real-time search performance depends on vector DB query optimization; large libraries may require pagination or filtering"],"requires":["VideoDB API credentials and authentication token","Video files in supported formats (MP4, MOV, WebM, etc.)","Network connectivity to VideoDB backend services","Sufficient storage quota for embeddings in vector database"],"input_types":["video files (MP4, MOV, WebM, AVI)","natural language queries (text)","timestamp ranges for partial video indexing"],"output_types":["ranked list of video segments with timestamps","relevance scores (0-1 float)","frame thumbnails and metadata for matched segments"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-videodb__cap_1","uri":"capability://data.processing.analysis.multilingual.video.transcription.with.speaker.diarization","name":"multilingual-video-transcription-with-speaker-diarization","description":"Automatically transcribes video audio into text across 100+ languages with speaker identification and timestamps. The system uses speech-to-text models with language detection, speaker diarization to separate multiple speakers, and alignment of transcripts to video frames. Output includes speaker labels, confidence scores, and precise timing for each spoken segment, enabling subtitle generation, searchability, and accessibility features.","intents":["Generate accurate subtitles in multiple languages from raw video footage","Extract and organize dialogue by speaker for editing or analysis workflows","Make video content searchable by transcribed speech content","Create accessibility features (captions) for international audiences"],"best_for":["Video production teams working with multilingual content","Content creators needing automated subtitle generation","Accessibility-focused platforms serving global audiences","Developers building video search or content analysis features"],"limitations":["Transcription accuracy varies by language, accent, and audio quality; noisy backgrounds degrade performance","Speaker diarization may fail with >5 simultaneous speakers or heavily overlapping dialogue","Processing time is real-time or slightly faster depending on audio quality and language complexity","Punctuation and capitalization are post-processed and may require manual correction for formal documents"],"requires":["VideoDB API credentials","Video file with audio track (mono, stereo, or multi-channel supported)","Target language codes (ISO 639-1 or 639-3 format)","Optional: speaker count hint for improved diarization"],"input_types":["video files with audio (MP4, MOV, WebM, MKV)","audio-only files (MP3, WAV, AAC, FLAC)","language codes (e.g., 'en', 'es', 'zh', 'ja')"],"output_types":["transcript JSON with speaker labels, timestamps, and confidence scores","SRT/VTT subtitle files","speaker-segmented transcript (one speaker per block)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-videodb__cap_2","uri":"capability://automation.workflow.ai.driven.video.editing.with.semantic.cuts","name":"ai-driven-video-editing-with-semantic-cuts","description":"Automates video editing decisions by analyzing content semantics to suggest or execute cuts, transitions, and scene organization. The system understands shot composition, pacing, dialogue flow, and visual continuity through frame analysis and transcript understanding, then generates edit decisions (cut points, transition types, duration adjustments) that can be applied directly to video timelines. Developers can specify editing rules (e.g., 'cut between speaker changes', 'add transitions at scene breaks') that are applied intelligently across the video.","intents":["Automatically generate rough cuts from raw footage based on content understanding","Suggest optimal cut points and transitions based on visual and audio analysis","Apply consistent editing rules across multiple videos without manual intervention","Reduce manual editing time for repetitive editing tasks like interview compilation or highlight reels"],"best_for":["Video production teams handling high volumes of raw footage","Content creators automating routine editing tasks (interviews, podcasts, vlogs)","Developers building video editing tools or automation platforms","Agencies producing templated video content at scale"],"limitations":["Editing suggestions are heuristic-based and may not match creative intent; human review is recommended for final output","Complex editing decisions (color grading, effects timing, music sync) are not supported; only structural cuts and transitions","Performance degrades with very long videos (>2 hours); may require segmentation","Requires well-lit, clear video content; poor quality footage produces unreliable suggestions"],"requires":["VideoDB API credentials","Video file in supported format (MP4, MOV, WebM)","Editing rule definitions (JSON schema specifying cut criteria, transition types)","Optional: transcript or metadata for content-aware editing"],"input_types":["video files (MP4, MOV, WebM, MKV)","editing rules (JSON with cut criteria, transition specifications)","optional: pre-generated transcripts or scene metadata"],"output_types":["edit decision list (EDL) or XML timeline format","JSON with cut points, transition types, and timing","preview video with suggested edits applied"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-videodb__cap_3","uri":"capability://image.visual.generative.media.synthesis.for.video.content","name":"generative-media-synthesis-for-video-content","description":"Generates synthetic video content (backgrounds, objects, scenes, transitions) using diffusion models or generative AI, integrated with video editing workflows. The system can fill in missing frames, extend scenes, generate background variations, or create transition effects based on text prompts or visual context. Generated content is automatically color-graded and composited to match surrounding footage, enabling seamless integration into edited videos.","intents":["Generate missing or damaged video frames to repair footage or extend scenes","Create background variations or alternative scene compositions without reshooting","Synthesize transition effects or visual elements based on creative direction","Extend video duration or fill gaps without additional footage"],"best_for":["Video production teams needing quick visual effects without VFX specialists","Content creators extending or repairing footage on tight budgets","Developers building AI-assisted video editing tools","Agencies producing high volumes of templated or variation-based content"],"limitations":["Generated content quality depends on model training and prompt specificity; photorealism is not guaranteed","Temporal consistency across multiple generated frames may show artifacts or flicker; requires post-processing","Synthesis is computationally expensive; generation time scales with resolution and duration","Generated content may not perfectly match lighting, color, or perspective of surrounding footage; manual color correction often needed","Legal/ethical concerns around synthetic media; watermarking or disclosure may be required"],"requires":["VideoDB API credentials with generative media access","Video file or frame sequence (MP4, MOV, image sequence)","Text prompts or visual reference images for generation","Sufficient API quota for generation requests"],"input_types":["video frames or frame sequences","text prompts describing desired content","reference images for style/composition matching","region masks for inpainting or localized generation"],"output_types":["generated video frames or sequences","composited video with generated content integrated","metadata including generation confidence and processing time"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-videodb__cap_4","uri":"capability://text.generation.language.voice.cloning.and.speech.synthesis.for.video","name":"voice-cloning-and-speech-synthesis-for-video","description":"Clones speaker voices from video audio and synthesizes new speech in the cloned voice, enabling dubbing, voice-over replacement, or multilingual audio generation. The system extracts voice characteristics from a reference audio sample, trains a lightweight voice model, and generates new speech with matching prosody, accent, and tone. Synthesized audio is automatically synchronized to video frames and mixed with background audio.","intents":["Generate dubbed audio in different languages while preserving original speaker voice characteristics","Replace or correct dialogue without reshooting or hiring voice actors","Create voice-over narration in a specific speaker's voice for consistency","Extend or modify dialogue to match edited video timing"],"best_for":["Video production teams handling multilingual content or dubbing","Content creators needing voice-over narration or dialogue replacement","Developers building video editing or localization tools","Agencies producing high volumes of localized video content"],"limitations":["Voice cloning quality depends on reference audio quality and duration; minimum 30-60 seconds of clear audio recommended","Synthesized speech may sound artificial or robotic for complex emotional delivery; emotional nuance is difficult to capture","Lip-sync between synthesized audio and original video requires additional processing; may not be perfect for close-ups","Ethical/legal concerns around voice cloning; consent and disclosure requirements vary by jurisdiction","Processing time scales with audio duration; real-time synthesis not supported"],"requires":["VideoDB API credentials with voice synthesis access","Reference audio sample (30+ seconds of clear speech from target speaker)","Target language code and text to synthesize","Video file for audio synchronization"],"input_types":["reference audio (MP3, WAV, AAC, extracted from video)","text to synthesize (plain text or script with timing)","target language code (ISO 639-1 format)","video file for timing and lip-sync reference"],"output_types":["synthesized audio file (WAV, MP3, AAC)","audio with timing metadata for video synchronization","composite video with synthesized audio mixed and lip-synced"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-videodb__cap_5","uri":"capability://safety.moderation.content.moderation.and.safety.filtering.for.video","name":"content-moderation-and-safety-filtering-for-video","description":"Analyzes video content for policy violations, inappropriate material, or safety concerns using computer vision and NLP models. The system scans frames for explicit content, violence, hate speech, or other flagged categories, generates moderation reports with timestamps and confidence scores, and can automatically blur, mute, or flag problematic segments. Developers can define custom moderation policies and thresholds.","intents":["Automatically detect and flag inappropriate content in user-generated video uploads","Generate moderation reports for compliance or review workflows","Blur or mute flagged content automatically before publishing","Monitor video libraries for policy violations at scale"],"best_for":["Video platforms handling user-generated content","Content moderation teams needing automated flagging and reporting","Developers building safety features into video applications","Enterprises ensuring brand safety in video content"],"limitations":["Moderation accuracy varies by content type and context; false positives/negatives are common for nuanced or cultural content","Models may have bias based on training data; certain groups or contexts may be over- or under-flagged","Custom policy definitions require careful specification; vague rules produce inconsistent results","Processing latency scales with video length; real-time moderation of live streams may not be feasible","Moderation decisions are probabilistic; human review is recommended for enforcement decisions"],"requires":["VideoDB API credentials with moderation access","Video file in supported format","Moderation policy definition (JSON with category thresholds and actions)","Optional: custom training data for domain-specific policies"],"input_types":["video files (MP4, MOV, WebM, MKV)","moderation policy rules (JSON with category definitions and thresholds)","optional: custom training examples for fine-tuning"],"output_types":["moderation report (JSON with flagged segments, categories, confidence scores, timestamps)","annotated video with flagged regions highlighted or blurred","compliance report for audit trails"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-videodb__cap_6","uri":"capability://tool.use.integration.mcp.protocol.integration.for.ai.agent.orchestration","name":"mcp-protocol-integration-for-ai-agent-orchestration","description":"Exposes VideoDB capabilities through the Model Context Protocol (MCP), enabling AI agents and LLMs to call video editing, search, and analysis functions as tools. The system implements MCP server endpoints for each capability, handles request/response serialization, manages authentication, and provides structured tool schemas that agents can discover and invoke. Agents can chain multiple VideoDB operations (e.g., search → transcribe → edit) in a single workflow.","intents":["Enable LLM-based agents to autonomously perform video editing and analysis tasks","Integrate VideoDB capabilities into multi-tool agent workflows","Allow natural language commands to trigger complex video operations","Build AI assistants that can reason about and manipulate video content"],"best_for":["Developers building AI agents with video manipulation capabilities","Teams integrating VideoDB into larger LLM-powered automation systems","Builders creating natural language interfaces to video editing","Enterprises automating video content workflows with AI orchestration"],"limitations":["MCP protocol overhead adds latency to each tool call; not suitable for real-time interactive editing","Agent reasoning about video operations is limited by LLM context window; complex multi-step workflows may exceed token limits","Tool schemas must be carefully designed for agent understanding; ambiguous or poorly-specified schemas lead to misuse","Error handling and recovery in agent workflows requires explicit implementation; cascading failures can occur","Cost scales with number of agent iterations; complex tasks may require many tool calls"],"requires":["MCP-compatible AI agent framework (e.g., Claude API with tool use, LangChain, AutoGPT)","VideoDB API credentials","MCP server running and accessible to agent","Agent with tool-calling capability"],"input_types":["natural language commands from agent","structured tool parameters (JSON)","video file references or URLs"],"output_types":["tool execution results (JSON)","structured data for agent reasoning","video files or editing instructions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-videodb__cap_7","uri":"capability://automation.workflow.batch.video.processing.with.job.queuing","name":"batch-video-processing-with-job-queuing","description":"Processes multiple videos asynchronously through a job queue system, enabling large-scale video analysis and editing without blocking. The system accepts batch job definitions (list of videos + operations), queues them for processing, provides job status tracking, and delivers results via webhooks or polling. Developers can monitor progress, retry failed jobs, and parallelize processing across multiple workers.","intents":["Process hundreds or thousands of videos through the same pipeline without manual intervention","Index large video libraries for search without blocking application","Generate transcripts and metadata for entire content catalogs","Apply consistent editing or moderation rules across video collections"],"best_for":["Video platforms with large content libraries","Content management systems handling bulk video operations","Developers building batch processing workflows","Teams automating video pipeline operations at scale"],"limitations":["Job processing time is non-deterministic; depends on queue depth, video complexity, and available resources","No guaranteed ordering or priority; all jobs processed in FIFO order unless priority queue is implemented","Webhook delivery is not guaranteed; requires idempotent result handling and retry logic","Storage for intermediate results (transcripts, embeddings) must be managed separately","Cost scales linearly with number of videos and operations; large batches can be expensive"],"requires":["VideoDB API credentials with batch processing access","Video files or URLs for batch processing","Job definition schema (JSON with video list and operation specifications)","Webhook endpoint for result delivery (optional; polling also supported)"],"input_types":["batch job definition (JSON with video list and operations)","video files or URLs","operation parameters (transcription languages, editing rules, etc.)"],"output_types":["job ID for tracking","job status (queued, processing, completed, failed)","batch results (transcripts, edits, metadata) via webhook or polling","error reports for failed videos"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["VideoDB API credentials and authentication token","Video files in supported formats (MP4, MOV, WebM, etc.)","Network connectivity to VideoDB backend services","Sufficient storage quota for embeddings in vector database","VideoDB API credentials","Video file with audio track (mono, stereo, or multi-channel supported)","Target language codes (ISO 639-1 or 639-3 format)","Optional: speaker count hint for improved diarization","Video file in supported format (MP4, MOV, WebM)","Editing rule definitions (JSON schema specifying cut criteria, transition types)"],"failure_modes":["Indexing latency scales with video duration and frame sampling rate; full HD video indexing may take minutes per hour of content","Semantic search accuracy depends on quality of underlying multimodal encoder; domain-specific content may require fine-tuned models","Vector database storage requirements grow linearly with video library size and embedding dimensionality","Real-time search performance depends on vector DB query optimization; large libraries may require pagination or filtering","Transcription accuracy varies by language, accent, and audio quality; noisy backgrounds degrade performance","Speaker diarization may fail with >5 simultaneous speakers or heavily overlapping dialogue","Processing time is real-time or slightly faster depending on audio quality and language complexity","Punctuation and capitalization are post-processed and may require manual correction for formal documents","Editing suggestions are heuristic-based and may not match creative intent; human review is recommended for final output","Complex editing decisions (color grading, effects timing, music sync) are not supported; 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