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Tubescout MCP Server

Developer ToolsLow Risk10.0MCP RegistryLocal
Free

Server data from the Official MCP Registry

YouTube as a research engine: keyless search, video intel, resilient transcripts, demand signals.

About

YouTube as a research engine: keyless search, video intel, resilient transcripts, demand signals.

Security Report

10.0
Low Risk10.0Low Risk

Valid MCP server (3 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.

12 files analyzed ยท 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

file_system

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Shell Command Execution

Runs commands on your machine. Be cautious โ€” only use if you trust this plugin.

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-not0lucky-tubescout": {
      "args": [
        "-y",
        "tubescout"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

TubeScout ๐Ÿ”ญ

Turn YouTube into a research engine for your AI agent. An MCP server (no API key) plus a skill pack that make Claude Code, Codex, and OpenCode search YouTube like a database, read transcripts at scale, and mine videos for evidence โ€” claims, numbers, demand signals โ€” instead of vibes.

Idea-engine tools scan Reddit and forums. YouTube is where founders show receipts โ€” revenue dashboards, playbooks, real numbers on camera โ€” and nothing mines it. TubeScout does.

Quickstart (60 seconds)

Claude Code

claude mcp add --scope user tubescout -- npx -y tubescout

Codex

codex mcp add tubescout -- npx -y tubescout

OpenCode โ€” add to ~/.config/opencode/opencode.json under "mcp":

"tubescout": { "type": "local", "command": ["npx", "-y", "tubescout"], "enabled": true }

That's it โ€” no API key, no config. Then ask your agent things like:

"Find the 5 most-viewed videos about n8n from the last month and summarize what people are struggling with."

Easiest all-in-one (Claude Code): install as a plugin โ€” MCP server + all 6 skills in two commands:

/plugin marketplace add not0lucky/tubescout
/plugin install tubescout@tubescout

Or install the skill pack manually (works for Claude Code, Codex, and OpenCode):

git clone https://github.com/not0lucky/tubescout && cd tubescout
./scripts/install-skills.sh   # installs into ~/.claude/skills, ~/.codex/skills, ~/.config/opencode/skills

Tools

ToolWhat it does
search_videosSearch with filters (upload window, duration, sort by views/date)
get_videoFull metadata + engagement (likesPer1kViews resonance signal)
get_transcriptPlain-text transcript via a resilient 3-strategy fallback chain
get_transcriptsBatch transcripts (up to 10 videos), per-video error tolerant
get_channel_videosChannel positioning + recent uploads with view counts
get_search_suggestionsYouTube autocomplete = real search demand for keyword research

Skills (the research methods)

SkillUse it to
/yt-breakdown <urls>Skeptic's analysis of videos: extract every claim and number, stress-test for incentives, survivorship bias, verifiability
/yt-idea-mine <niche>Mine a niche for product ideas backed by demand signals + pains real builders describe on camera
/yt-validate <idea>Go/no-go verdict: demand, saturation, what competitors' numbers actually show
/yt-channel-intel <channel>Read a channel's strategy: cadence, outliers, what performs vs what they publish
/yt-playbook <tutorial url>Turn a tutorial into executable steps โ€” exact commands, settings, and the gotchas said in passing โ€” adapted to your stack
/yt-gap <niche>Find demand-vs-supply gaps: heavily searched topics served by weak, old, or misfit videos โ€” for content plans or product angles

All skills are context-aware: they read the conversation for what you're building, your stack, and videos already analyzed, and tailor verdicts to your actual leverage instead of giving generic advice.

See a real /yt-breakdown run on three "how I make $X/month" videos โ€” including what survived the skeptic pass and what didn't.

How it works (honestly)

There's no magic here, and that's the point:

  • youtubei.js talks to YouTube's internal InnerTube API โ€” the same one the site uses. No key, no quota.
  • Transcripts are YouTube's own captions, fetched through a fallback chain: the ANDROID-client timedtext track โ†’ the InnerTube transcript endpoint (known to 400 intermittently โ€” retried with backoff) โ†’ local yt-dlp if you have it. Each response tells you which source served it.
  • All analysis happens in your agent. The server ships data; the skills ship method.

Limitations

  • Run it locally. YouTube aggressively rate-limits datacenter IPs โ€” this is a local stdio server by design, not a hosted service.
  • YouTube changes internals without notice; when it breaks, update (npx always pulls latest) and file an issue with the failing video ID.
  • Videos with captions disabled can't be transcribed (rare; the error says so explicitly).
  • Caption scraping lives in YouTube ToS gray area โ€” fine for local research tooling, don't build a hosted paid product on it.

Development

npm install && npm run build
npm test          # unit tests (offline)
npm run test:live # live smoke tests against real videos โ€” run before publishing
npm run inspect   # MCP Inspector against the built server

MIT โ€” see LICENSE.


Built by Anir โ€” I automate things. More at agramprojects.com.

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