YouTube as a research engine: keyless search, video intel, resilient transcripts, demand signals.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
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 β claims, numbers, demand signals β instead of vibes.
Idea-engine tools scan Reddit and forums. YouTube is where founders show receipts β revenue dashboards, playbooks, real numbers on camera β and nothing mines it. TubeScout does.
Claude Code
Codex
OpenCode β add to ~/.config/opencode/opencode.json under "mcp":
That's it β 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 β MCP server + all 6 skills in two commands:
Or install the skill pack manually (works for Claude Code, Codex, and OpenCode):
| 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 |
| 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 β exact commands, settings, and the gotchas said in passing β adapted to your stack |
/yt-gap <niche> | Find demand-vs-supply gaps: heavily searched topics served by weak, old, or misfit videos β 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 β including what survived the skeptic pass and what didn't.
There's no magic here, and that's the point:
yt-dlp if you have it. Each response tells you which source served it.npx always pulls latest) and file an issue with the failing video ID.MIT β see LICENSE.
Built by Anir β I automate things. More at agramprojects.com.
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