flask

Feedback layer for video.

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Feedback layer for video. Reviewers talk through feedback; agents read it as structured comments.

    How to connect

    How to connect flask to Claude

    The server address to paste into Claude settings:

    https://api.flask.do/api/mcp/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect flask to ChatGPT

    The server address to paste into ChatGPT settings:

    https://api.flask.do/api/mcp/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect flask to Cursor

    The server address to paste into Cursor settings:

    https://api.flask.do/api/mcp/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (17)

    Raw names from tools/list. Only developers need these.

    contentsBrowse contents. Without folder_id: lists what's at the team's top level (root) — both folders and assets that live directly at root. With folder_id: opens that folder and returns its child folders and assets. Each asset item carries asset_id (the asset), version_id (its current version), version_count, its media fields, url (always the newest version) and version_url.
    feedback_listList top-level feedback on an asset (pass the asset id for its current version, or a version id for that exact version). Returns each item's text body, tags (by name), author, timestamp, and reply_count. Recording items are ONE comment per recording: the text body is the AI-organized feedback, and the recording object adds the full verbatim transcript plus visual_references (the pointing phrases with the recording time each was spoken). Items with visual attachments carry images (attached image files) and/or has_drawing: true (a drawing made on the asset) - call get_annotated_frames to see them. Use feedback_get to read full reply threads.
    feedback_getGet a single feedback item with its full reply thread. Returns the item's text body (for recordings: the AI-organized feedback), tags (by name), author, video timestamp, the recording's full verbatim transcript, visual_references (the pointing phrases - "this", "over here" - with the recording time each was spoken; feed them to get_frame to see what was being pointed at), visual attachments (images / has_drawing - render them with get_annotated_frames), and all nested replies.
    wait_for_feedbackWait for NEW feedback on an asset. Blocks up to timeout_seconds (default 45) and returns as soon as feedback newer than `since` arrives, or times out with an empty list. To listen continuously, call it again with the returned next_since. Use this after uploading a video for review instead of repeatedly calling feedback_list.
    feedback_postCreate a feedback comment on an asset, as the connected user. Pass an asset id (the comment lands on its current version) or a version id (that exact version). PREFER passing timestamp (seconds into the video) whenever the source material has one - e.g. when importing feedback from an email like 'at 0:42 the logo is wrong', convert 0:42 to 42. Without timestamp the comment is a general (non-anchored) note. tags applies the team's EXISTING tags by name (see the tags tool); statuses are tags too. Use reply_to to reply in an existing thread. Cannot attach recordings, drawings, or images.
    feedback_updateEdit an existing feedback item. content (replaces the text) and timestamp (video time in seconds) can only be changed on the connected user's OWN comments. add_tags / remove_tags apply the team's existing tags by NAME and work on ANY feedback item you can comment on (labels and statuses are collaborative - e.g. add the 'Done' tag to mark feedback resolved). Provide at least one of content, timestamp, add_tags, remove_tags.
    get_annotated_framesSupplementary visuals for a feedback item. For recording items: the media under review with the reviewer's drawing rendered in, plus their shared SCREEN when they demonstrated something (e.g. a Photoshop/Figma mockup); webcam frames are NOT included, and the transcript is returned marked [FRAME N]. Also works for text comments that carry visuals: a standalone drawing (has_drawing: true) is rendered onto the frame it was drawn over, and attached images (images array) are returned as frames. If a visual reference is still unclear, call get_frame to drill into a specific moment (at) or spoken word/phrase (word). Returns up to 12 images.
    get_frameDrill into a recording for MORE visual detail: get the exact frame at a specific time (at) or when a specific word/phrase was spoken (word). Use when get_annotated_frames did not show what a reference ('this','here','that') means, or to see a moment the transcript mentions. Returns the asset frame at that moment (plus the active shared screen, if any) and a transcript snippet around it.
    asset_statusCheck the processing status of a video. Pass the version id an upload returned (asset_id), or an asset id (resolves to its current version). Returns progress percentage for videos being processed, confirms it is ready, or reports no_versions for an asset that has no media yet.
    permission_getSee who has access to a folder or an asset and their permission levels (full_access, comment, view, none). Returns the connected user's effective access, the creator, plus link and team-member default access levels. Pass folder_id for a folder, or asset_id for an asset (an asset id or any of its version ids - permissions live on the asset, versions inherit them).
    searchSearch across your team's folders, assets, and feedback by text. Always returns all three categories together.
    recent_activityGet the latest comments across your team, newest first. Returns a mix of text and recording comments with author, timestamp, and a link to jump into the conversation. Each item has a `type` field ("text" or "recording") plus the asset it belongs to (container_id / container_title), the version it was left on (asset_id = version id, version number), so you can read feedback across many videos in one call. Scope with folder_id to read one client's/project's feedback, narrow the window with since/until, and page with offset. For counts and trends use feedback_stats instead.
    tagsList the team's tags and show the share of each tag (plus an 'untagged' bucket) across a scope. Scope defaults to the whole team; pass folder_id to narrow to one folder (its whole subtree), or asset_id to narrow to one asset (an asset id counts feedback across ALL its versions; a version id widens to its asset). Shares are count_with_tag / total_elements and do NOT sum to 100% because elements can carry multiple tags.
    upload_videoUpload a video from a public URL (a direct video file link or a Google Drive share link) into Flask. Flask downloads it, stores it, and starts processing. Creates a new asset (container_id + url) with the file as its first version (asset_id / version_id), or a new version of an existing asset with version_of. Share the returned asset URL while processing; poll asset_status(version_id) until status is "ready" before reporting processing complete. folder_id is OPTIONAL: omit it to upload to the team's top level (root). Free to use for up to 100 MCP uploads; beyond that the user needs a plan at https://flask.do/plan. You still need edit access on the folder if one is given. For a LOCAL file on disk, use upload_file_start instead.
    upload_file_startStart uploading a LOCAL video file from the user's machine into Flask. Creates the asset immediately (container_id + shareable url, live before any bytes move) with the file as its version (asset_id / version_id), or a new version of an existing asset with version_of, and returns a presigned upload URL. After calling this, upload the file bytes with the curl command from next_step, then call upload_file_complete. folder_id is OPTIONAL: omit it to upload to the team's top level (root). Max file size 5GB. Free to use for up to 100 MCP uploads; beyond that the user needs a plan at https://flask.do/plan. You still need edit access on the folder if one is given.
    upload_file_completeFinalize a local file upload started with upload_file_start. Call this only AFTER the curl PUT has finished successfully. Verifies the file landed in storage and starts video processing. Safe to retry.
    feedback_statsAggregate feedback statistics across many assets in ONE call - counts per asset and per version (v1, v2, ...), split by type (text/recording), tag, and author, with first/last feedback timestamps. Scope to a folder (folder_id), one asset (asset_id - an asset id or any of its version ids; the whole asset is reported, version by version), or the whole team; narrow the time window with from/to. Each item carries container_id (the asset) and its versions. Use this for trend questions like 'are v1 notes going down across this client's recent videos' or 'what tags dominate this folder' instead of calling feedback_list per asset. For reading the feedback text itself across assets, use recent_activity.