F
licenseA
qualityD
maintenanceEnables AI assistants to analyze YouTube channels, videos, transcripts, and content strategy through structured tool calls.
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18 npm
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yt-outlier-mcp
by phillipmex
This server helps you identify viral YouTube videos on small channels where the format (not the audience) drove the views, enabling you to replicate those formats.
find_outliers: Search YouTube by topic phrase to surface videos on small channels (≤100K subs) that massively outperform both their subscriber base (≥5:1 views:subs ratio) and the channel's own typical upload performance — proving the format is algorithmically replicable.get_video_structure: Given a video ID or URL, extract its replicable format elements: duration, tags, chapters (parsed from description timestamps), full description, and transcript — so you can copy the structure, not just the topic.get_comment_signal: Fetch top comments from a video and get demand metrics (question-asking comments, "please make more"/"part 2" phrasing) to verify the topic has genuine unmet audience demand.search_niche_sweep: Run the outlier search across up to 8 niches simultaneously using a phrase template (e.g. "beginner mistakes {niche}") and rank all hits by views:subs ratio — identifying which niche currently has the most replicable breakout format.get_channel_baseline: Starting from a channel (by ID, @handle, or URL), compute its median recent-upload view baseline and flag which of its recent uploads are outliers — useful when you already have a specific channel to investigate.
Provides tools to find outlier YouTube videos on small channels, extract video structure and transcripts, and analyze comments for demand signals.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@yt-outlier-mcp find outliers for 'beginner mistakes sourdough'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Input | Default | Meaning |
| (required) | Topic phrase, e.g. |
| 100,000 | Max channel subscribers |
| 100,000 | Min video views |
| 5 | Min views:subs ratio |
| 365 | Freshness window (older outliers are stale evidence) |
| 3 | Video views vs. median of channel's other recent uploads |
| 0 (off) | Min fraction of query terms found in title/description/tags; |
| 10 | Cap on returned 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: SanTech Content Engine MCP Server
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).
Input | Default | Meaning |
| (required) | Video ID or URL (watch/shorts/youtu.be forms) |
| true | Fetch the transcript |
| 15,000 | Truncation cap |
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.
Input | Default | Meaning |
| (required) | Video ID or URL |
| 30 | Top comments to fetch (max 100) |
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.
Input | Default | Meaning |
| (required) | Phrase containing |
| (required) | 1–8 niches to substitute |
| 5 | Cap per niche |
filters | same as |
|
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.
Input | Default | Meaning |
| (required) | Channel ID ( |
| 15 | Recent uploads to fetch for the baseline (3–50) |
| 3 | Flag uploads at ≥ this multiple of the channel median |
Setup
npm install
npm run buildConfigure in a client (Claude Code example):
claude mcp add yt-outliers -e YOUTUBE_API_KEY=<key> -- node <abs-path>/dist/index.jsBYO 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) andget_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.
Available Tools
4 toolsfind_outliersFind YouTube outlier videosA
Search YouTube for a topic phrase and return videos on small channels that hugely outperform the channel's subscriber base and its own recent uploads — evidence the FORMAT drove the views (replicable by a new channel), not an existing audience. Defaults encode the Icon Method criteria: ≥100K views, channel ≤100K subs, ≥5:1 views:subs, uploaded within the last year. Costs ~110-130 YouTube API quota units per call (free daily quota: 10,000).
Inputs: query (required), maxSubs, minRatio, minViews, +3 more.
get_comment_signalRead a video's comment demand signalA
Fetch a video's top comments (relevance-ordered) plus simple demand metrics: how many ask questions and how many use demand phrasing ('please make...', 'part 2', 'how do you...'). Icon Method verification step: comments prove the topic has unmet demand, not just views. Costs 1 YouTube API quota unit.
Inputs: video (required), maxComments.
get_video_structureGet a video's replicable structureA
Fetch what makes an outlier video's format copyable: duration, chapters (parsed from the description), tags, and the transcript. Icon Method verification step: extract the structure, don't guess it. Costs 1 YouTube API quota unit (transcript is fetched outside the API at zero quota and may be unavailable for some videos).
Inputs: video (required), includeTranscript, maxTranscriptChars.
search_niche_sweepSweep a phrase template across nichesA
Run the outlier search once per niche by substituting each niche into a phrase template (e.g. "beginner mistakes {niche}" across ['sourdough', 'bonsai', 'leathercraft']) and rank the hits across all niches. Answers: which hobby cluster has a replicable breakout format right now? EXPENSIVE: each niche costs a full search (~110-130 quota units), so an 8-niche sweep uses ~10% of the 10,000-unit daily free quota.
Inputs: niches (required), template (required), maxSubs, minRatio, +4 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
A4/5.0
Scored across 4 tools
Disambiguation5/5
Each tool targets a distinct step in the outlier analysis workflow: finding outliers, analyzing comments, extracting video structure, and running batch niche searches. No two tools overlap in purpose.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (find_outliers, get_comment_signal, get_video_structure, search_niche_sweep), making them predictable.
Tool Count4/5
With 4 tools, the set is slightly lean but appropriate for a specialized YouTube outlier analysis server. Each tool addresses a core function without unnecessary redundancy.
Completeness4/5
The tools cover the main outlier identification and validation pipeline: search, comment analysis, and structure extraction. Missing channel-level details or result persistence are minor gaps given the focused scope.
Related MCP Connectors
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