io.github.not0lucky/tubescout

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

Open source Open in the app JSON README (API)

About

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

Details

Kind: MCP servers
Topic: Social & content
Publisher: not0lucky
Origin: official
Category: ferramentas
Transport: local
Version: 0.1.1
Forks: 1
Last push: 2026-08-27T14:07:27Z
Repository state: ativo
Language: TypeScript
License: MIT
Added: 2026-08-29 04:00:55
Updated: 2026-08-29 04:00:55
Origin id: io.github.not0lucky/tubescout

README

# TubeScout 🔭

**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 — claims, numbers, demand signals — instead of vibes.

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

## Quickstart (60 seconds)

**Claude Code**

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

**Codex**

```bash
codex mcp add tubescout -- npx -y tubescout
```

**OpenCode** — add to `~/.config/opencode/opencode.json` under `"mcp"`:

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

That's it — 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** — 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):

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

## Tools

| Tool | What it does |
|---|---|
| `search_videos` | Search with filters (upload window, duration, sort by views/date) |
| `get_video` | Full metadata + engagement (`likesPer1kViews` resonance signal) |
| `get_transcript` | Plain-text transcript via a resilient 3-strategy fallback chain |
| `get_transcripts` | Batch transcripts (up to 10 videos), per-video error tolerant |
| `get_channel_videos` | Channel positioning + recent uploads with view counts |
| `get_search_suggestions` | YouTube autocomplete = real search demand for keyword research |

## Skills (the research methods)

| Skill | Use 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 — exact commands, settings, and the gotchas said in passing — adapted to your stack |
| `/yt-gap <niche>` | Find demand-vs-supply gaps: heavily searched topics served by weak, old, or misfit videos — 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](examples/breakdown-3-videos.md) on three "how I make $X/month" videos — 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 — 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 → the InnerTube transcript endpoint (known to 400 intermittently — retried with backoff) → 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 — 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 — fine for local research tooling, don't build a hosted paid product on it.

## Development

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

MIT — see [LICENSE](LICENSE).

---

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