Reka

Upload a video from a URL. Returns a video_id. Local file paths are not accepted; upload files outside the MCP server and pass a reachable video_url. The upload runs asynchronously — poll get_video until status is 'uploaded', then call index_video to enable search and analysis.

List all videos in your account, or filter to a specific group by passing group_id. Shows upload status and which features have been indexed for each video. Each video's 'url' is a short-lived HTTPS presigned URL (expires within hours) — fetch immediately and do not store; call list_videos or get_video again for a fresh URL when needed.

Get detailed information about a video including upload status, metadata (duration, resolution, fps), and per-feature indexing status. Use this to check if upload or indexing is complete. The 'url' field is a short-lived HTTPS presigned URL (expires within hours) — fetch immediately and do not store; call get_video again for a fresh URL when needed.

Update a video's display name, title, description, or move it to a different group. At least one field must be provided. To remove a video from its group, pass group_id as null.

Permanently delete a video and all its indexed data (transcript, captions, embeddings, etc.). This cannot be undone.

Create a new video group. Groups organize videos into collections. Returns the new group's ID and name.

List all video groups. Use list_videos with a group_id to see videos in a specific group.

Delete a video group. Videos in the group are not deleted — they are simply removed from the group.

Index a video for search, QA, or full analysis. Processes the video through a pipeline of AI features. Typically takes 3-7 minutes; longer for long videos or the 'full' pipeline. Times out after 10 minutes by default. Pipelines: - search_only: transcription + captions + embeddings (enables search_videos) - qa_only: transcription + captions (enables ask_video) - full: transcription + captions + em

Find WHEN and WHERE something happens across your videos. Returns timestamped results ranked by relevance — use these timestamps as start/end in ask_video for focused analysis. This is the recommended first step for most questions. Instead of asking ask_video about the entire video, search first to narrow down the relevant moments. Each result's 'video_url' is a short-lived HTTPS presigned URL

Ask a question about one or more videos with visual analysis. Most effective on focused time ranges — use start/end to specify the segment to analyze. BEFORE calling this tool, read the reka://docs/guide resource for recommended workflows. In most cases, you should first: - search_videos to find WHEN something happens, then pass those timestamps here as start/end - segment_video to detect and l

Detect objects in a video segment using text prompts. Describe what to look for and get per-frame detections with bounding boxes and confidence scores. Prompt tips: - Use broad, visual categories: 'animal', 'vehicle', 'person', 'text on screen' - Specific labels ('rabbit', 'Toyota') are less reliable — the detector matches visual patterns, not semantic concepts - Best for confirming whether a c

Get the spoken words in a video. Use this instead of ask_video when you need to read what was said — it returns the actual text, not a summary. Use start/end to narrow results for long videos. Requires the transcript feature to be indexed.

Get AI-generated visual descriptions of what happens on screen. Use this to understand the visual content without watching — each caption describes a short segment with timestamps. Use start/end to narrow results. Requires the captions feature (qa_only or full pipeline).

Get detected scene boundaries with start/end timestamps. Use this to understand the video's structure, then pass scene timestamps as start/end to: - ask_video for per-scene contextual analysis - segment_video to detect specific objects per scene (scenes typically fit in segment_video's 15s max range) Requires transcript indexed with scene detection (on by default; skipped only if index_video was

List available video analysis features with their dependencies and descriptions. Use this to understand what features exist and what pipelines to use with index_video.

Start here. Get a compact overview of a video: metadata, which features are indexed, a transcript preview, and scene count. Use this to decide which tools to call next — then use segment_video to detect specific objects in time ranges of interest.