yt-outlier-mcp

MCP server that finds YouTube outlier videos: videos on small channels (≤100K subs) with one video massively outperforming both the channel's subscriber base (≥5:1 views:subs) and the channel's own recent uploads. That signature means the recommendation algorithm rewarded the format, not an existing audience — so the format is replicable by a new channel.

The method is the "Icon Method" qualifying criteria proven manually on @Before-You-Start (see hobby-channel/IDEAS.md); this server automates it as one MCP tool. Origin: idea #2 in _ideas/next-batch.html (paid MCP servers).

Tool: find_outliers

Pipeline per call: search.list (order=viewCount, the expensive call) → batch videos.list + channels.list → cheap-filter by views/subs/ratio → for survivors, pull the uploads playlist and compare against the channel's median recent-upload views (the outlier-vs-baseline check that separates a breakout format from a big channel's normal video).

Output per outlier: URL, views, subs, ratio, channel median views, outlier factor, queryRelevance (fraction of query terms found in title/description/tags — always reported, filtered only if minQueryRelevance > 0; costs zero extra quota since the snippet is already fetched), comments-enabled flag (comments are the manual demand-signal step), channel video count, plus total quota units consumed.

Related MCP server: YouTube MCP

Tool: get_video_structure

Icon Method verification step 2 — extract the replicable format instead of guessing it. Takes a video ID or URL; returns duration, tags, chapters (parsed from 0:00 Intro-style description lines), the description, and the transcript. Costs 1 quota unit; the transcript itself is fetched outside the Data API at zero quota (captions.download needs owner OAuth, so the server asks the InnerTube player endpoint as the ANDROID client — unofficial, returns transcript: null gracefully if YouTube ever gates it).

Tool: get_comment_signal

Icon Method verification step 3 — comments prove unmet demand, not just views. Returns the top relevance-ordered comments (author, text, likes, replies) plus quick counts: comments asking questions and comments using demand phrasing ("please make…", "part 2", "how do you…"). Handles comments-disabled videos gracefully. Costs 1 quota unit.

Tool: search_niche_sweep

Runs find_outliers once per niche by substituting each niche into a phrase template, then ranks every hit across all niches by views:subs ratio — the niche that keeps appearing up top is where the replicable format lives. Expensive: each niche is a full search (~110–130 units), max 8 niches per sweep. Per-niche API errors are recorded without killing the sweep; a quota-exhausted error aborts the remaining niches with a note.

Tool: get_channel_baseline

The inverse entry point: you already have a suspect channel (from a competitor, a comment, another tool) instead of a topic query. Computes the channel's baseline — median views of its recent uploads — and scores every recent upload against it, flagging outliers. Cheap: ~3 quota units (no search.list call). Accepts channel ID, @handle, or channel URL.

Setup

npm install
npm run build

Configure in a client (Claude Code example):

claude mcp add yt-outliers -e YOUTUBE_API_KEY=<key> -- node <abs-path>/dist/index.js

BYO key: needs a YouTube Data API v3 key (.env.example). Free quota is 10,000 units/day; one find_outliers call costs ~110–130 units (search=100, everything else 1/call), so ~75–90 searches/day. The BYO-key model is what makes this sellable without a Google quota-extension audit.

Roadmap

• Live-test tool against real niches (2026-07-10: 4 real outliers on "beginner mistakes sourdough", 110 units/call as predicted)

• Phase 2 tools: get_video_structure (chapters/transcript) and get_comment_signal (top comments → demand resonance) to automate Icon Method verification steps 2–3 (2026-07-10, live-tested)

• search_niche_sweep: rotate one phrase template across hobby clusters (2026-07-10, live-tested: 2-niche sweep = 210 units, cross-niche ranking works)

• List on Smithery (2026-07-10): live at smithery.ai/servers/phillipmex3/yt-outlier-mcp. MCPize deferred (their SDK/hosting required; actual rev share 80%, not the 85% marketed) — revisit if Smithery shows install signal.

Publishing note

Smithery's registry requires each tools[] entry in manifest.json to carry an inputSchema, but npx @anthropic-ai/mcpb pack rejects that key as invalid. Workaround used here: pack the bundle from a manifest without the schemas, then replace manifest.json inside the .mcpb (it's a plain zip) with the schema-bearing version in this repo before smithery mcp publish.