MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
作者提供的说明(英文原文)
Context hygiene for Claude Code: drift detection, re-read dedup, and token analytics.
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。
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。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
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": {
"sipcode": {
"command": "npx",
"args": [
"-y",
"sipcode",
"sipcode-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add sipcode -- npx -y sipcode sipcode-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"sipcode": {
"command": "npx",
"args": [
"-y",
"sipcode",
"sipcode-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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"sipcode": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"sipcode",
"sipcode-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。
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
注册名称已通过 GitHub 账户 Anuj7411 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
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)。
GitHub 仓库最后更新于–,最新版本(1.7.1)于25天前进入注册中心。
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 Node.js。
同一分类中有:MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
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
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
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。
Sipcode
Keep Claude Code's context clean for sharper answers and lower cost, automatically.
Website · Compare · Releases
npm i -g sipcode
sipcode init
That is it. Your next Claude Code session will use Sipcode automatically. Verify a few minutes later:
sipcode drift
If the output reads no drift, context health stable, Sipcode is doing its job.
Already installed? Keep it fresh. Sipcode makes zero network calls, so it can't pop an "update available" notice. Build a weekly habit: run
sipcode update(ornpm i -g sipcode@latest) to pull the latest fixes. Full details in Updating Sipcode.
Sipcode is a free command-line tool that sits between you and Claude Code. It does three things:
It is open source under the MIT license. It makes zero network calls during normal use. Your data never leaves your laptop.
| Sipcode | Tool A | Tool B | Tool C | |
|---|---|---|---|---|
| Approach | Live PreToolUse hook | Cross-session memory store | Static methodology | RAG retrieval server |
| Caps verbose tool output | ✓ | ✗ | docs only | ✗ |
| Dedups same-session re-reads | ✓ | ✗ | docs only | ✗ |
| Mid-session install support | ✓ Verified Warm-Fill | n/a | n/a | n/a |
| Zero false-dedup by construction | ✓ | n/a | n/a | n/a |
| Reproducible benchmark on locked corpus | ✓ 62.6% median (20 synthetic tasks) | ✗ | ✗ | ✗ |
| Self-introspection MCP tools | ✓ 16 tools | ✗ | ✗ | partial |
| Agents covered | Claude Code (all features); OpenAI Codex (spend analytics and usage limits, no hook) | not compared | not compared | not compared |
| Zero network calls in normal use | ✓ | ✓ | n/a | ✗ |
| MIT licensed | ✓ | ✓ | ✓ | ✓ |
Sipcode is complementary to memory tools that persist context across sessions (Tool A in the table). Sipcode keeps each individual session clean. They solve different problems. Run both for maximum effect.
Full feature-by-feature comparison with tool names revealed: anuj7411.github.io/sipcode/compare
If you use Claude Code for real work, you have already felt this:
Sipcode measures the bloat, then removes the parts Claude does not need. Run sipcode benchmark on any machine and you will see the same 62.6% median reduction on the locked 20-task corpus. That corpus is synthetic: scripted pairs of Claude Code transcripts (without and with Sipcode), not recordings of live sessions. It shows what Sipcode's mechanisms remove from a modeled session. Your own number depends on your workload, and sipcode impact measures it on your real sessions.
After installing, here are the five commands that show you what Sipcode is doing.
sipcode driftTells you if your current Claude Code session is drifting from your normal usage pattern. Bloated context, repeated reads, stale signals are all flagged here.
✓ Sipcode drift: no drift, context health stable vs your recent baseline.
By default it checks your most recent session, across Claude Code and Codex. --session checks a specific one, --here limits it to sessions from the folder you are in, and --agent claude-code or --agent codex picks the tool.
sipcode proxy --statsShows what Sipcode caught in the current session, broken down by rewriter.
Sipcode proxy, rewrite stats
total rewrites: 144 est. tokens saved: ~288,685 (heuristic) signal kept: 67% (med), weighted across all rewrites
sipcode benchmarkRuns the locked 20-task corpus and produces a reproducible savings number. Anyone, anywhere can run this and get 62.6%. The transcripts are scripted, not recorded from live sessions, so read it as a model of Sipcode's mechanisms rather than a measured bill (see METHODOLOGY).
62.6% median savings on a locked 20-task corpus
range 37.4% to 80.6% 3,567,170 tokens $22.48
sipcode todayDaily spend summary. Tokens used, sessions, output ratio, comparison to your 30-day median.
spend so far $1.20 across 4 sessions
tokens so far 943.8K output ratio 3.7%
sipcode forecastMonth-end projection based on your last 14 days.
projected month-end $17,674 (range $14,139 to $21,208)
Sipcode also reads OpenAI Codex CLI session logs (~/.codex/sessions and ~/.codex/archived_sessions, or the folder CODEX_HOME points to). For Codex this is spend analytics and usage limits: Sipcode reports what your Codex sessions used, where the tokens went, and how close you are to your plan's limits. It does not change what Codex does.
Commands that read Codex: stats, today, forecast, trend, impact, why, receipt, drift, regress and limits (Codex only), plus the session tools in the MCP server (they take an optional agent input: "claude-code" or "codex") and get_codex_limits.
--agent codex shows Codex only. --agent claude-code shows Claude Code only.--agent flag and both tools installed, stats, today, forecast, trend and impact print one section per tool. stats, today and forecast end with one combined line, in this shape: Both tools: <total tokens> tokens · ~$<Claude Code> + ~$<Codex>
why, receipt and drift report on the most recent session across both tools. When the other tool has a recent session too, a one-line hint names it and the flag that shows it.--here works for Codex too: it matches the working folder Codex recorded for each session.--json covers one tool per call. Without --agent it covers Claude Code and prints a note on stderr when Codex logs exist too. Add --agent codex for Codex JSON.--agent flag on a machine with Cursor and Codex, Sipcode shows Codex (it does not read Cursor's session logs).How Codex tokens are counted. Sipcode reads Codex's per-request usage records, or, in logs from older Codex versions, the change in the running total Codex logs after each request. Cached input is part of Codex's input count, so Sipcode never bills cached tokens twice. A request copied into a resumed or forked session file is counted once. On the maintainer's machine, Sipcode's per-file totals equal Codex's own final total_token_usage on all 15 real session logs, and an independent counter that shares no code with Sipcode (scripts/verify-counts.mjs) cross-checks every period command against the same logs.
Prices. Codex models are priced from OpenAI's API price table (developers.openai.com/api/docs/pricing, and each Codex model's page under developers.openai.com/api/docs/models, as of 2026-10-09), including the long-context rate for prompts over 272,000 input tokens. A model with no known price is shown as unpriced: its tokens are still counted, a line under the cost says how many were left out of it, and it is never shown as $0.
Not supported for Codex yet: writing rules to AGENTS.md (rules, and the rules step of init), registering the MCP server in Codex, the proxy and hooks (proxy, hygiene), estimate and benchmark. rules --agent codex and estimate --agent codex stop with error E009 instead of showing Claude Code data.
Compressed Codex logs. Codex compresses older session logs to .jsonl.zst. Sipcode reads them exactly like plain logs on Node.js 22.15 or newer. On an older Node.js it skips them, and every report starts with a warning that says how many it could not read and that reading them needs Node.js 22.15 or newer. A damaged or unreadable log gets the same kind of warning. With --json the warning is a note on stderr and the JSON is unchanged.
sipcode limitsCodex writes your plan's usage-limit readings into its own logs after each request. sipcode limits reads them, with zero network calls, and answers three questions: when will I hit my limit, what used it, and did it shrink.
Example output of sipcode limits --since all (the tokens-per-1% history here goes back further than the default 90 days):
sipcode limits · Codex · plan: go · as of 2h ago (your last Codex request)
Weekly limit 62% used resets Wed 14 Oct 14:00 at this pace: runs out Tue 13 Oct ~18:00 30-day limit 22% used resets Mon 2 Nov at this pace: lasts until reset
What used your weekly limit (since Wed 7 Oct) 0199a1b2-c3d4-7a11 sipcode 31% 3.1M tokens 0199a0f7-11aa-7b02 my-app 17% 1.4M tokens codex-auto-review (9 requests) 6% unattributed (no Codex requests on this computer) 8%
Tokens per 1% of your weekly limit (gpt-5.4) Mar 28: 425K Apr 1: 357K Apr 13: 77K Possible limit cut around Apr 13: 5.1x fewer tokens per 1%. Limits cover your whole account. Codex use on other devices or in the cloud is not in these logs and can look like a cut.
unattributed, because limits are shared with your other devices and Codex cloud.只显示了开头部分;其余内容请在仓库中阅读