FlowScope UX Flow Analyser

Name: FlowScope UX Flow Analyser
Author: DavidNgugi

作者：DavidNgugi版本 0.1.1i★ –i在你的电脑上运行

注册名称io.github.DavidNgugi/flowscope

安装

概览

作者提供的说明（英文原文）

Turn YouTube product demos into structured UX flow reports, and compare demos side by side.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 营销与社交媒体
你需要填写：FLOWSCOPE_API_TOKEN

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： FLOWSCOPE_API_URL, FLOWSCOPE_API_TOKEN, FLOWSCOPE_MAX_WAIT

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "flowscope": {
      "command": "uvx",
      "args": [
        "flowscope-mcp"
      ],
      "env": {
        "FLOWSCOPE_API_URL": "your-value",
        "FLOWSCOPE_API_TOKEN": "your-value",
        "FLOWSCOPE_MAX_WAIT": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add flowscope --env FLOWSCOPE_API_URL=your-value --env FLOWSCOPE_API_TOKEN=your-value --env FLOWSCOPE_MAX_WAIT=your-value -- uvx flowscope-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "flowscope": {
      "command": "uvx",
      "args": [
        "flowscope-mcp"
      ],
      "env": {
        "FLOWSCOPE_API_URL": "your-value",
        "FLOWSCOPE_API_TOKEN": "your-value",
        "FLOWSCOPE_MAX_WAIT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "flowscope": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "flowscope-mcp"
      ],
      "env": {
        "FLOWSCOPE_API_URL": "your-value",
        "FLOWSCOPE_API_TOKEN": "your-value",
        "FLOWSCOPE_MAX_WAIT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

FLOWSCOPE_API_URL env
Base URL of the running FlowScope backend. '/api' is appended if absent.
FLOWSCOPE_API_TOKEN 机密 env
Bearer token, only needed when the backend sits behind an authenticating proxy.
FLOWSCOPE_MAX_WAIT env
Default seconds that flowscope_analyze_video waits for a job before returning a job id.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 DavidNgugi 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“营销与社交媒体”中的其他服务器比较i

星标数多于此分类中其他 2954 个服务器的 0%
发布到注册中心的时间比此分类中其他 2954 个服务器的 50% 更新
基础检查结果等于或优于此分类中其他 2954 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

FlowScope UX Flow Analyser 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：FLOWSCOPE_API_TOKEN

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.1.1）于30天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

MCP YouTube Transcript

作者：jkawamoto

An MCP server retrieving transcripts of YouTube videos

在你的电脑上运行★ 495MIT9天前更新

安装

Google Analytics MCP

作者：surendranb

GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.

在你的电脑上运行★ 244MIT9天前更新

安装

BibiGPT YouTube Video Summarizer

作者：JimmyLv

AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.

在云端运行★ 137MIT9天前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

flowscope-mcp

An MCP server that exposes the FlowScope UX-analysis pipeline to any LLM client. Speaks MCP protocol 2026-07-28 and ships two spec-portable Agent Skills.

uvx flowscope-mcp        # stdio server; needs a local FlowScope backend

FlowScope takes a YouTube product-demo URL, downloads the video, transcribes it, extracts and de-duplicates screenshots of each distinct screen, describes every screen with a vision model, and synthesises a step-by-step UX flow. This package turns that pipeline into tools an agent can call.

Ask your agent something like:

Compare the onboarding flows in these two videos: and

Tear down the checkout UX in this demo:

Requirements

This server is a client of a running FlowScope backend; it does not run the pipeline itself. Start the backend first:

cd backend

uvicorn app.main:app --port 8000

The backend needs ffmpeg, a Python environment with backend/requirements.txt, and one LLM provider key in backend/.env. The server's flowscope_health_check tool reports exactly which of these is missing, so you can ask your agent to check rather than guessing.

Install

As a Claude Code plugin

One step, and it brings the skills, the prompts, and the server registration:

/plugin marketplace add DavidNgugi/flowscope

/plugin install flowscope@flowscope

From PyPI

Nothing needs a permanent install — uvx runs it in a throwaway environment. uv is the only prerequisite (brew install uv):

uvx flowscope-mcp                 # starts a stdio server

uvx flowscope-mcp --version # confirm the published version uvx flowscope-mcp@0.1.0 # pin, when reproducibility matters

From a git URL or a local checkout

uvx --from /absolute/path/to/flowscope/mcp flowscope-mcp

uvx --from "git+https://github.com/DavidNgugi/flowscope.git@main#subdirectory=mcp" flowscope-mcp

Environment

Variable	Default	Purpose
`FLOWSCOPE_API_URL`	`http://127.0.0.1:8000`	Backend base URL. `/api` is appended if absent, so either form works.
`FLOWSCOPE_API_TOKEN`	_(empty)_	Sent as `Authorization: Bearer …`. Only needed behind an authenticating proxy.
`FLOWSCOPE_HTTP_TIMEOUT`	`30`	Seconds for ordinary requests.
`FLOWSCOPE_MAX_WAIT`	`900`	Default wait budget for `flowscope_analyze_video`.
`FLOWSCOPE_POLL_INTERVAL`	`5`	Seconds between job-status polls.

These are read from the process environment on each call. The MCP Python SDK v2 deliberately stopped reading MCP_* variables and .env files, so set them in your client's server configuration instead.

Point a harness at it

The config shapes genuinely differ — VS Code's own file uses `servers` where every other client uses `mcpServers`, Codex is TOML, and Claude Desktop does not expand ${VAR} placeholders — so generating the file is more reliable than copying a snippet:

uvx --from flowscope-mcp flowscope-mcp-install --help   # every client + its path

uvx --from flowscope-mcp flowscope-mcp-install print # portable JSON, writes nothing

uvx --from flowscope-mcp flowscope-mcp-install claude-code uvx --from flowscope-mcp flowscope-mcp-install cursor uvx --from flowscope-mcp flowscope-mcp-install vscode uvx --from flowscope-mcp flowscope-mcp-install codex uvx --from flowscope-mcp flowscope-mcp-install gemini uvx --from flowscope-mcp flowscope-mcp-install claude-desktop

It merges into an existing config file and leaves other servers alone.

Harness	File	Key	Syntax
Claude Code	`.mcp.json`, `~/.claude.json`	`mcpServers`	JSON
Claude Desktop	`claude_desktop_config.json`	`mcpServers`	JSON
Cursor	`.cursor/mcp.json`, `~/.cursor/mcp.json`	`mcpServers`	JSON
VS Code	`.vscode/mcp.json`	`servers`	JSON
Codex CLI	`~/.codex/config.toml`	`[mcp_servers.flowscope]`	TOML
Gemini CLI	`~/.gemini/settings.json`	`mcpServers`	JSON

VS Code additionally reads the portable /.mcp.json shape (key mcpServers), which flowscope-mcp-install vscode-portable writes when you want one config shared with Claude Code and Cursor.

Remote / shared deployment

Run one server for several clients, or for a harness that cannot spawn a process:

flowscope-mcp --transport streamable-http --host 127.0.0.1 --port 8765

Clients connect to POST http://127.0.0.1:8765/mcp:

claude mcp add --transport http flowscope http://127.0.0.1:8765/mcp

docs/install.md has the full per-harness reference, the placeholder syntax each one accepts, and the two traps that most often break an install.

Use it

Restart your client, open a new chat, and check the install first:

Check whether FlowScope is ready to use.

That runs flowscope_health_check and names anything missing — usually ffmpeg or an LLM provider key on the backend host. Then ask for what you want.

Example prompts

One video, fully analysed

Tear down the onboarding UX in this demo:

What does this product actually do, based on this demo? Walk me through the flow screen by screen:

Analyse and tell me where a first-time user would get stuck.

A specific question about a flow

In this demo, how many steps does signup take, and what does each one ask for?

Does this demo show any error or empty states?

What's above the fold on the first screen, and what's the primary call to action?

Several videos, compared

Compare the onboarding flows in these two demos. Where do they diverge, and what's the trade-off each one makes?

-

I'm designing a checkout flow. What do these two demos do differently, and what should I borrow?

Which of these three gets a new user to first value fastest, judging by the demos?

Working with what already exists

What videos has FlowScope already analysed?

Show me the screenshot of the pricing screen from that report.

You do not need to phrase these carefully. Analyses are cached by YouTube video id, so a vague prompt costs a lookup rather than a re-analysis — the agent checks the cache before spending anything.

What happens after you press enter

Analysing a video downloads it, transcribes it, extracts and de-duplicates screenshots, then makes one vision call per distinct screen plus a synthesis call. A five-minute demo typically yields 15–40 screens. Expect 2–10 minutes and real money per video.

flowscope_analyze_video waits by default and usually returns the finished report directly, so one prompt is normally enough. If the video needs longer than the budget it returns status: "running" with a video_id, and the agent should poll flowscope_job_status. If it resubmits the URL instead, stop it — that starts duplicate work.

Answers are grounded, or should be

Reports have three layers: ux_insights (the synthesis), flow_steps (the ordered user path), and frames (the evidence — one entry per screen, with its purpose, UI elements, and aligned narration). A good answer leads with the insights and cites the screen behind each claim. The bundled skills enforce this, including saying "the demo does not show this" rather than inferring.

docs/usage.md has the full walkthrough, prompt patterns that produce better answers, and a troubleshooting table.

Tools

Ten tools, split between read-only inspection and actions that cost money.

Tool	Annotations	What it does
`flowscope_health_check`	read-only	Backend up? ffmpeg, yt-dlp, provider key present?
`flowscope_list_videos`	read-only	Every stored video with its latest job status.
`flowscope_job_status`	read-only	Progress of one analysis job.
`flowscope_get_report`	read-only	The finished UX report: flow, insights, per-screen findings.
`flowscope_frame_image`	read-only	The actual screenshot for one screen, as an image.
`flowscope_compare_videos`	open-world	Cross-video common patterns, divergences, stage matrix.
`flowscope_analyze_video`	open-world	Download and analyse one or more URLs. Costs money.
`flowscope_retry_video`	idempotent	Re-queue a failed job; completed stages are reused.
`flowscope_reanalyze_video`	destructive	Discard derived results and redo analysis.
`flowscope_delete_video`	destructive	Remove a video; optionally delete media from disk.

Plus two prompts (flowscope_ux_teardown, flowscope_compare_flows) and two resources (flowscope://videos, flowscope://videos/{video_id}/report).

Cost and time

Analysing a video downloads it, transcribes it, and makes one vision call per distinct screen plus a synthesis call. That is minutes and real money per video. Two things protect against waste:

Results are cached by YouTube video id. flowscope_list_videos reveals existing analyses, and resubmitting a finished video is free and instant.
flowscope_analyze_video checks the cache before starting work and only submits what is actually new.

The bundled skill teaches an agent to check the cache first.

Transports

flowscope-mcp                                  # stdio (default)

flowscope-mcp --transport streamable-http --port 8765

Streamable HTTP serves POST /mcp. The SDK serves the 2026-07-28 protocol revision and the older handshake era from the same endpoint, so legacy clients work without extra configuration. Legacy clients create server-side sessions, which are stored in-process — if you run more than one worker behind a load balancer, enable sticky routing.

Skills

Two Agent Skills ship alongside the server, written to the open specification so they install into any compatible client:

Skill	Use it when
`analyzing-product-demo-ux`	You want a teardown of one recorded flow — how onboarding, signup, or checkout works, screen by screen.
`comparing-product-demo-ux`	You want several products compared: shared conventions, real divergences, and what is worth borrowing.

They carry the operational knowledge the tool descriptions cannot: check the health and the cache before spending money, read the synthesis rather than re-deriving it from the raw transcript, say "the demo does not show this" rather than inferring, and treat an auto-caption's product names with suspicion.

The skills and the server are independent — the skills are useful on a report you already have, and the server

只显示了开头部分；其余内容请在仓库中阅读