Flask Video Feedback

Flask Video Feedback

Video feedback for AI agents via Flask (flask.do). Agents upload renders for human review and get the feedback back as transcribed, timestamped comments over MCP, with annotated frames and versioned revisions.

Category
访问服务器

README

Flask plugin for Claude Code

Flask is the feedback layer for video, built for the agentic loop: your agent uploads a render and shares the link instantly. The reviewer doesn't type - they hit record and talk through the video (voice, camera, screen, drawing on frames), and Flask turns the recording into structured, timestamped comments with transcripts. The agent reads that feedback and iterates, pushing each revision as a new version of the same asset. Typed comments work too - recordings are the advantage, not a requirement.

This plugin connects the Flask MCP server and teaches the agent the full review loop.

Install

One command connects the Flask MCP server (Claude Code, Cursor) and installs the review-loop skill for any agent that reads skills:

npx flask-feedback

Prefer to wire things yourself? Per-client instructions below.

Claude Code

/plugin marketplace add tryflask/skills
/plugin install flask@flask

Then authenticate once: /mcp -> flask -> complete the browser sign-in.

No plugin manager? Connect the MCP server directly:

claude mcp add --transport http flask https://api.flask.do/api/mcp/mcp

Cursor, Codex, and other agents

Install the review-loop skill (works across skills-compatible clients):

npx skills add tryflask/skills

Then connect the MCP server:

  • Cursor: Install MCP Server
  • claude.ai / Claude Desktop: Settings -> Connectors -> add https://api.flask.do/api/mcp/mcp
  • Any MCP client: Streamable HTTP at https://api.flask.do/api/mcp/mcp (OAuth sign-in opens in the browser on first use)

What the agent can do

Tool What it does
contents, search, recent_activity Browse folders/assets, search, latest team feedback
feedback_list, feedback_get Read feedback with tags, timestamps, recording transcripts (transcript: "full" for whole recording)
wait_for_feedback Long-poll - returns new feedback the moment it's left
get_annotated_frames Returns the video frames a recording pointed at / drew on, as images with the drawing rendered in, plus the transcript marked [FRAME N] - resolves "this"/"here"/drawn-circle references to actual pixels
upload_file_start / upload_file_complete Upload a local video (5GB max) via presigned URL; share link available the instant the upload starts
upload_video Import from a public URL or Google Drive link
version_of (param on uploads) Upload as a new version of an existing asset - one stable link for the whole iteration
asset_status, tags, permission_get Processing status, tag distribution, folder access

The server is read-only except for uploads - it can never edit or delete anything.

The loop in practice

agent renders video -> upload_file_start -> user gets flask.do link instantly
user records feedback on the video -> wait_for_feedback returns it (transcribed)
agent implements changes -> uploads v2 with version_of -> same link shows v2

Docs and support

  • Server documentation: https://flask.do/mcp
  • Support: hello@flask.do

This repo is kept in sync with the MCP server. Tool list and behavior described here mirror https://flask.do/mcp, which is the source of truth.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选