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PopNx MCPMCP servers for people who use AI · explained in plain words

yt

by ArasanDevversion 0.1.1i★ –iRuns on your computer
Registry nameio.github.ArasanDev/yt

Install

Overview

Description from the maker (original English)

YouTube transcripts, transcript search, and channel monitoring for agents.

PopNx MCP’s three pillars

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

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

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).
  • The registry lists no key to fill in.

You need

Which app do you use?

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": {
    "yt": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/arasandev/yt:0.1.1",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add yt -- docker run -i --rm ghcr.io/arasandev/yt:0.1.1 mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "yt": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/arasandev/yt:0.1.1",
        "mcp"
      ]
    }
  }
}

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": {
    "yt": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/arasandev/yt:0.1.1",
        "mcp"
      ]
    }
  }
}

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.

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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account ArasanDev, 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 2982 servers in this category
  • Published to the registry more recently than 82% of the other 2982 servers in this category
  • Basic checks equal to or better than 100% of the other 2982 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 yt 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

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 7 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker 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:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

An MCP server retrieving transcripts of YouTube videos

Runs on your computer★ 495MITUpdated 10 days ago
Install

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

Runs on your computer★ 244MITUpdated 10 days ago
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.

yt

YouTube transcripts for agents and humans. One static Go binary, no API key, no AI dependencies, no runtime requirements.

  • Fetches a video's own caption tracks (manual or auto-generated) via YouTube's internal Innertube API, using ANDROID/IOS client contexts — the WEB-client caption URLs have been pot-token gated since mid-2025 and return empty bodies.
  • Cascades innertube-android → innertube-ios → watch-page so a single throttled endpoint doesn't take the fetch down; every response reports which method won (source).
  • Channel monitoring: yt channel @handle (or list_channel_videos) resolves a handle and lists the latest uploads with exact publish timestamps from the channel RSS feed, so an agent can diff against a watermark and see what is new.
  • Same binary is the MCP server: yt mcp, or bare yt when stdin is piped (the shape MCP clients spawn). Bare yt at a terminal prints help.
  • MCP responses are designed for agent context budgets: bounded payloads, structural truncation with exact resume pointers, time/cue-addressed ranges, server-side search, typed errors, and session-level caching so one get_video_info + get_transcript + search_transcript share a single player fetch.

Dependencies: cobra (CLI), modelcontextprotocol/go-sdk (official MCP SDK), and golang.org/x/term (TTY detection). The YouTube fetch layer is stdlib only — net/http, encoding/json, encoding/xml.

Layout

The repository root is the Go module (github.com/ArasanDev/yt) and the product root.

cmd/yt/               binary entry point

internal/cli/ cobra commands and flags internal/mcp/ MCP tool schemas, handlers, safety warnings internal/youtube/ Innertube client, captions, channels, cache, typed failures internal/search/ pure transcript search internal/render/ block/cue rendering, SRT, truncation metadata internal/transcript/ shared Segment/Transcript model and timestamp helpers internal/apperr/ typed error contract (code, retryable, suggestion) internal/version/ build version, commit, and date

CI lives in .github/workflows/ci.yml and runs gofmt, go vet, go test -race, a native build, and a cross-build matrix for the release targets.

Dependencies point inward: cli and mcp depend on youtube, search, render, and transcript; youtube depends on apperr and transcript; search and render depend only on transcript. The acquisition layer does not import the adapters.

Install

Homebrew (macOS/Linux)
brew trust arasandev/tap          # Homebrew 7+ trusts third-party taps explicitly

brew install --cask arasandev/tap/yt

Install script (macOS/Linux)
curl -fsSL https://raw.githubusercontent.com/ArasanDev/yt/main/install.sh | sh

The script downloads the latest release for your OS/architecture, verifies it against checksums.txt, and installs to /usr/local/bin when writable, otherwise ~/.local/bin. Override the target with YT_INSTALL_DIR and pin a version with YT_VERSION:

YT_VERSION=v0.1.0 YT_INSTALL_DIR="$HOME/bin" \

curl -fsSL https://raw.githubusercontent.com/ArasanDev/yt/main/install.sh | sh

Go developers
go install github.com/ArasanDev/yt/cmd/yt@latest

go install records the module version, so yt --version reports the release even though ldflags cannot be injected. Use make install for a local build that also stamps commit and date.

Docker
docker run --rm -i ghcr.io/arasandev/yt:latest mcp

CLI use:

docker run --rm ghcr.io/arasandev/yt:latest transcript dQw4w9WgXcQ

The image is a distroless static build. With no TTY, bare yt already serves MCP, so the default container command is mcp.

Linux packages

Release assets include .deb, .rpm, and .apk packages for amd64 and arm64:

sudo dpkg -i yt_<version>_linux_amd64.deb

sudo rpm -i yt_ _linux_amd64.rpm sudo apk add --allow-untrusted yt_ _linux_amd64.apk

Prebuilt binaries

Every release publishes archives, Linux packages, and a checksums.txt:

https://github.com/ArasanDev/yt/releases

Targets: darwin/linux/windows on amd64 and arm64; Linux also ships deb/rpm/apk packages.

From source
git clone https://github.com/ArasanDev/yt.git

cd yt make build # ./bin/yt make install # into GOBIN with version/commit/date stamped

Release builds stamp a version, commit, and date (the default is dev):

go build -trimpath -ldflags "\

-X github.com/ArasanDev/yt/internal/version.Version=v0.1.0 \ -X github.com/ArasanDev/yt/internal/version.Commit=$(git rev-parse --short HEAD) \ -X github.com/ArasanDev/yt/internal/version.Date=$(date -u +%Y-%m-%dT%H:%M:%SZ)" \ -o bin/yt ./cmd/yt yt --version

CLI

yt transcript <url-or-id>                       # transcript to stdout

yt transcript -l en,es -t # language fallback + [MM:SS] stamps yt transcript --start 1274 --end 1290 # time range yt transcript -f srt -o subs.srt # SRT file yt transcript -f json --max-chars 2000 # cue JSON, bounded yt transcript --bare # body only, no metadata header yt transcript --start-cue 120 --max-cues 40 # resumable cue window yt search "harness engineering" # timestamped hits, no full read yt search "M C P" --match-mode any # spelled acronyms collapse to mcp yt search "state of the art" --match-mode phrase yt search "harness" --context-cues 2 # human output shows context yt tracks # list caption languages yt info # title, channel, duration, views, chapters yt channel @aiDotEngineer --limit 10 # latest uploads (exact timestamps) yt channel @aiDotEngineer --since 2026-10-01 --json yt doctor # probe which acquisition clients work yt --version # build version (or module version under go install)

Accepts watch?v=, youtu.be/, /shorts/, /live/, /embed/ URLs or a bare 11-char video ID. By default, -f text includes a metadata header and, when needed, a truncation trailer. Use --bare for body-only stdout; truncation is then reported on stderr. Fetch/renderer notes always go to stderr.

CLI usage errors and upstream failures are typed. cmd/yt prints code, retryable, retry_after_seconds, and suggestion to stderr while keeping stdout clean. Invalid formats, negative limits, and inverted time ranges fail before any network request.

MCP

yt mcp serves stdio JSON-RPC; so does bare yt when stdin is not a terminal (how MCP clients spawn a server), while bare yt at a prompt prints help. Call get_video_info first: it reports caption availability, live status, and an estimated transcript size, so you can decide between reading the whole transcript, searching it, or reading a range.

ToolPurpose
list_channel_videosLatest uploads for a channel (@handle/URL/UC…), from RSS: exact publish times, titles, links, description excerpts.
get_video_infoMetadata + caption summary + transcript size estimate + chapters. No transcript download.
get_transcriptTranscript text or cue JSON, bounded and addressable.
search_transcriptFind a topic with timestamps and context; match_mode all/any/phrase; zero results explain themselves.
list_tracksCaption tracks. Deprecated — use get_video_info.captions.

For monitoring, call list_channel_videos and pass the previous response's newest_published as since. Filtering is strictly published > since, so no video is reported twice. The feed exposes at most 15 entries and has no duration or view counts; those need get_video_info on a specific video.

Key get_transcript parameters: languages (CSV, default en; original for the uploader's own language), allow_translation, format (text|json), granularity (block default |cue), timestamp_mode (block default |cue|none), start_seconds/end_seconds, start_cue/max_cues, max_chars (default 50000; bounds the transcript body only).

Truncation is never silent: the header carries TRUNCATED next_start_seconds=… next_cue_index=…, structured metadata carries the same fields, and next_* values are exact (not rounded), so resuming at them never drops the boundary cue. max_cues clipping is reported even when end_seconds is also set. Errors come back as isError results with a machine-readable error.code (bot_check, rate_limited, no_captions, bad_request, …) plus retryable and a suggestion.

The transcript body is returned in both content and structuredContent for client compatibility, so a response is roughly twice the body size; returned_chars measures one copy.

Auto-dubbed videos list one auto-generated track per language. A track whose language differs from the uploader's known original is reported as translated=true, whether it is manual or ASR, so machine-translated speech is never presented as the speaker's words. get_video_info.captions.original_language names that track. If the player response carries no audio-track metadata and multiple caption languages exist, the original is unknown: languages=original returns an actionable no_matching_language error instead of guessing, and original_language may be empty. Request a specific language in that case.

Wire into any agent:

devin mcp add yt -- yt mcp          # Devin CLI

claude mcp add yt -- yt mcp # Claude Code

or raw mcpServers JSON (Devin, Claude Desktop, Cursor, Windsurf, …):

{"mcpServers": {"yt": {"command": "yt", "args": ["mcp"]}}}

Releasing and distribution

A v* tag triggers .github/workflows/release.yml, which:

  1. runs the test suite and GoReleaser (archives, Linux packages, checksums, GitHub Release);
  2. builds and pushes a multi-arch ghcr.io/arasandev/yt image;
  3. stamps the version into server.json and publishes to the MCP registry.
git tag v0.1.0

git push origin v0.1.0

Homebrew cask updates are skipped unless the repository has a HOMEBREW_TAP_GITHUB_TOKEN secret: a fine-grained PAT with contents: write on ArasanDev/homebrew-tap. Without it, the release still ships binaries, Docker, and MCP metadata; add the cask manually or add the secret and re-run.

The MCP registry job is best-effort (continue-on-error). GHCR packages are private by default: set the yt package to public once in the package settings, then re-run the job. The registry checks the image annotation io.modelcontextprotocol.server.name against server.json's name, so those two strings must stay in sync.

Useful local checks before tagging:

goreleaser check

goreleaser release --snapshot --clean # artifacts in dist/, never published docker build --build-arg VERSION=dev -t yt:dev .

Development

make build          # ./bin/yt

make test # go test -race ./... make check # gofmt + go vet + go test -race make snapshot # local GoReleaser snapshot make clean

See AGENTS.md for the architecture, contracts, and conventions agents must preserve. In short: stdout is data, stderr is diagnostics; errors are typed; truncation is never silent and resume pointers are exact; provenance and content_trust labels are not optional; tests never hit the network.

Caveats

  • IP blocking: datacenter IPs (AWS/GCP/VPS) get blocked o

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

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