MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Descripción del creador (original en inglés)
Context hygiene for Claude Code: drift detection, re-read dedup, and token analytics.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"sipcode": {
"command": "npx",
"args": [
"-y",
"sipcode",
"sipcode-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add sipcode -- npx -y sipcode sipcode-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"sipcode": {
"command": "npx",
"args": [
"-y",
"sipcode",
"sipcode-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"sipcode": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"sipcode",
"sipcode-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub Anuj7411, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (1.7.1) llegó al registro hace 25 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.Solo se muestra el principio; lee el resto en el repo