FlowScope UX Flow Analyser

Name: FlowScope UX Flow Analyser
Author: DavidNgugi

by DavidNgugiversion 0.1.1i★ –iRuns on your computer

Registry nameio.github.DavidNgugi/flowscope

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 23/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Marketing & social media
You need to fill in: FLOWSCOPE_API_TOKEN

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: FLOWSCOPE_API_URL, FLOWSCOPE_API_TOKEN, FLOWSCOPE_MAX_WAIT

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into 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"
      }
    }
  }
}

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

FLOWSCOPE_API_URL env
Base URL of the running FlowScope backend. '/api' is appended if absent.
FLOWSCOPE_API_TOKEN secret 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.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account DavidNgugi, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Marketing & social mediai

More stars than 0% of the other 3597 servers in this category
Published to the registry more recently than 48% of the other 3597 servers in this category
Basic checks equal to or better than 100% of the other 3597 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is FlowScope UX Flow Analyser safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: FLOWSCOPE_API_TOKEN

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 32 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

MCP YouTube Transcript

An MCP server retrieving transcripts of YouTube videos

by jkawamotoGrowing★ 495Updated 11 days ago

Runs on your computer

Install

Google Analytics MCP

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

by surendranbGrowing↓ 1.1K/wk★ 244Updated 11 days ago

Runs on your computer

Install

BibiGPT YouTube Video Summarizer

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

by JimmyLvEarly★ 137Updated 11 days ago

Runs in the cloud✓ bibigpt.co

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

FlowScope UX Flow Analyser

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Marketing & social mediai

Common questions

Alternatives

MCP YouTube Transcript

Google Analytics MCP

BibiGPT YouTube Video Summarizer

README from the maker

flowscope-mcp

Requirements

Install

As a Claude Code plugin

From PyPI

From a git URL or a local checkout

Environment

Point a harness at it

Use it

Example prompts

What happens after you press enter

Answers are grounded, or should be

Tools

Cost and time

Transports

Skills