bug2context

Name: bug2context
Author: emanueld92

作者：emanueld92版本 0.1.1i★ –i在你的电脑上运行

注册名称io.github.emanueld92/bug2context

安装

概览

作者提供的说明（英文原文）

Turn bug screen recordings into chronological context for AI agents: frames, OCR, logcat, voice.

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: 其他

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）。
注册中心没有列出需要填写的密钥。

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": {
    "bug2context": {
      "command": "uvx",
      "args": [
        "bug2context",
        "mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add bug2context -- uvx bug2context mcp

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": {
    "bug2context": {
      "command": "uvx",
      "args": [
        "bug2context",
        "mcp"
      ]
    }
  }
}

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": {
    "bug2context": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "bug2context",
        "mcp"
      ]
    }
  }
}

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。

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 emanueld92 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“其他”中的其他服务器比较i

星标数多于此分类中其他 32476 个服务器的 0%
发布到注册中心的时间比此分类中其他 32476 个服务器的 70% 更新
基础检查结果等于或优于此分类中其他 32476 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

bug2context 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？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

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16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：Spec Driven Development, Remote Desktop Commander, zanadir

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

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新

安装

zanadir

作者：MustacheCase

Find what a repository's CI is missing, and which security checks cannot fail the build.

在你的电脑上运行今天更新

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。

bug2context

Turns a screen recording of a bug into structured, chronological context an AI agent can actually read.

Screenshots lose the story: bugs happen fast, a still frame has no timeline, and the logs and the narration live somewhere else entirely. LLMs don't take video. This distills a recording into one markdown chronology — key frames, the text on screen, device logs and spoken narration, all on the same clock.

No SDK. Works on any video, including one someone sent you over WhatsApp.
Local-first. Nothing leaves your machine. No account, no API key, no cloud.
MCP-native. Claude Code calls it as a tool; you just point at the file.

uvx bug2context               # guided menu

uvx bug2context process bug.mp4 --out bundle/

Running it with no arguments opens a menu — record an Android device, process a video you already have, or list previous bundles — and writes to ~/.bug2context/, which is where the MCP server looks. Everything it does is reachable through the flags below; it just stops you having to remember them.

What comes out

- `[00:00]` 🖼 `frames/frame_001_00-00.png` — start of recording

OCR: Cart 2 items

[00:04] 🖼 frames/frame_002_00-04.png — screen changed OCR: Applying discount..
[00:04] 📋 W/DiscountEngine: rate field missing
[00:06] 🖼 frames/frame_003_00-06.png — screen changed OCR: TypeError: cannot read 'rate'
[00:06] 📋 E/RideService: NullPointerException at RideRequest.kt:142
[00:06] 📋 F/AndroidRuntime: FATAL EXCEPTION: main
[00:06] 🎙 "ahí está, explota al aplicar el descuento"

A full run is in examples/report.md.

bundle/

├── report.md the chronology above ├── frames/ the key frames, named by timestamp └── meta.json the same data, structured, for programmatic use

Install

Requires ffmpeg on PATH (brew install ffmpeg).

uvx --from 'bug2context[ocr]' bug2context          # OCR: Apple Vision on macOS, tesseract elsewhere

uvx --from 'bug2context[ocr,audio]' bug2context # plus spoken narration

Or from source:

git clone https://github.com/emanueld92/bug2context

cd bug2context uv sync --extra ocr --extra audio

Every stage is optional and degrades quietly: no OCR backend still gives you frames, no audio track still gives you the visual timeline.

Use it from Claude Code

claude mcp add bug2context -- uvx --from 'bug2context[ocr]' bug2context-mcp

Working from a local clone instead: uvx --from /path/to/bug2context bug2context-mcp.

Then just ask: *"analiza este video del bug: ~/Desktop/crash.mp4"*.

Tool	Purpose
`analyze_bug_video`	Video → chronological report (returns the text itself)
`get_frame`	Fetch one frame as an image so the agent can look at it
`list_bundles`	Previously processed bundles, newest first

Bundles default to ~/.bug2context/ - /.

Android: screen + logcat in one command

uv run bug2context record --seconds 60 --package com.example.app --out bundle/

Reproduce the bug while it records. Both streams start from a single host timestamp — that anchor is what makes a stack trace land next to the frame showing the crash.

Pass `--package`. Level filtering alone is not enough on a real phone: a 2 min capture held 7,634 W/E/F lines and *none* came from the app under test — they were all AppOpsControllerImpl, GNSSMGT and friends. Scoping to the app's process left 35. Crash tags (AndroidRuntime, DEBUG) are kept regardless of process, since a crash report is the whole point.

Logs are captured unfiltered and scoped afterwards, so an app that crashes and restarts still has its death recorded. Consecutive identical entries fold into one with a (×N) count — framework chatter arrives in bursts of fifteen.

Already have a log file? Merge it into any video:

uv run bug2context process bug.mp4 --logcat logcat.txt --log-tag RideService

Reads both -v time and -v threadtime. --log-levels defaults to WEF and --log-max-lines to 80, ranked by severity so a cap never trades the crash for boot chatter. --log-pid scopes to a process when you already know it. If the device clock and the recorder disagree, --log-offset shifts everything by N seconds; with no --video-started-at, the first log entry becomes the anchor.

screenrecord caps at 3 minutes, so record refuses longer rather than handing back a silently truncated video. Ctrl-C cuts a recording short safely — the file is finalised on the device first, because killing the local adb leaves one ffprobe cannot open.

Videos over 10 minutes are refused with the ffmpeg command to trim them (--max-duration 0 overrides). Nothing scales badly with length except time, but it scales linearly: decoding alone runs at about a third of real time.

Narration

uv run bug2context process bug.mp4 --transcribe --language es

Off by default: it downloads a model on first use and is the slowest stage, while most recordings have no voice-over. Skipped automatically when there is no audio track, or the track is quieter than −50 dB — whisper invents confident sentences out of silence, so that guard is about output quality, not just speed.

How frames get picked

ffmpeg over-produces candidates (scene cuts and a fixed interval, so slowly changing screens are not skipped), then perceptual hashing collapses the near-duplicates. --max-frames caps the result, always keeping the opening frame and then whichever changed most.

Flag	Default	Notes
`--scene-threshold`	`0.08`	ffmpeg scene score for a cut
`--interval-seconds`	`1.0`	forced sample when no cut fires
`--max-frames`	auto	one frame per 2 s, between 20 and 40
`--phash-distance`	`4`	below this, frames count as duplicates
`--ocr-upscale`	`2`	enlarge before OCR; skipped above 1600 px wide

Measured end to end, OCR on:

Source	Result	Time
synthetic 3 min, 1080×1920	19 frames	8.5 s
Android capture, 2 min, 720×1612 @ ~12 fps	118 candidates → 20 frames	16 s
macOS capture, 90 s, 2880×1864 @ 60 fps	97 candidates → 18 frames	86 s

Recognised text is cached by frame content in ~/.cache/bug2context/, so re-running a video with different settings only pays for what actually changed — measured 9.0 s cold against 3.3 s warm on a 44 s clip, identical output. Extracting the same video twice yields byte-identical frames, which is what makes the cache safe. Delete the directory to reset it.

Retina desktop recordings are the slow case and there is no trick to remove: the cost is decoding 60 fps at 5.4 megapixels (26 s) plus OCR, which Vision charges at ~1.6 s per frame regardless of size. Budget roughly real time for those; phone captures stay far under it.

On --ocr-upscale: a stack trace on a recompressed 1080×1920 frame read as Null PointerSxception Ride Requestkt14 at 1× and correctly as NullPointerException Ride Request kt 142 at 3×. Raise it for badly recompressed sources — ask for the video as a file, not as a WhatsApp video. It is skipped on frames already 1600 px or wider, where it changed nothing measurable while building a 5760×3728 image per frame.

OCR also drops lines that are debris rather than text — », .lll (100 4, a misread status-bar clock. On a real capture that removed 41 of 210 report lines and no real text. A line that looks like a failure is never dropped.

Known limitation

Perceptual hashing compares visual *structure*. It sees navigation, dialogs and error banners, but it cannot detect a small text-only change on an otherwise identical layout — measured on 1080×1920 those differences fall inside the noise floor at every hash size, so no --phash-distance value separates them.

OCR does not rescue this: it runs on frames that survive deduplication, so a screen already collapsed is never read. Reading every candidate instead measured 48–59 s for a 3 min video, over the entire time budget. If your bug *is* a small text change on an identical screen, record cropped or at lower resolution so the text occupies more of the frame.

Development

uv sync --extra ocr

uv run pytest -q

183 tests. Device recording is mocked on purpose — a test suite should not record anyone's phone. Whisper is opt-in via BUG2CONTEXT_TEST_WHISPER=1 so the suite stays offline.

License

MIT — see LICENSE.

bug2context

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“其他”中的其他服务器比较i

常见问题

替代选择

Spec Driven Development

Remote Desktop Commander

zanadir

作者的 README

bug2context

What comes out

Install

Use it from Claude Code

Android: screen + logcat in one command

Narration

How frames get picked

Known limitation

Development

License