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vidlens-mcp

VidLens MCP — YouTube as a queryable database for AI agents. 41 tools, zero config, three-tier fallback, semantic + visual search.

npm120/wk
Updated
Apr 5, 2026

Quick Install

npx -y vidlens-mcp

VidLens — YouTube as a queryable database for AI agents

npm License MCP 41 tools Zero Config


🔍 What is VidLens?

Stop watching 10 videos to answer one question. VidLens searches YouTube, reads the transcripts, and synthesizes what creators actually said — across multiple videos, with timestamps, benchmark charts, and sources.

VidLens is a Model Context Protocol server that gives AI agents deep, reliable access to YouTube. Not just transcripts — full intelligence: search, analysis, visual search, and auto-generated comparison charts.

No API key required to start. Every tool has a three-tier fallback chain (YouTube API → yt-dlp → page extraction) so nothing breaks when quota runs out or keys aren't configured.

Try it — paste any of these into Claude:

"I'm thinking about buying the M5 Max MacBook Pro. Search YouTube for top tech reviewers and tell me what they're saying. Is it worth the upgrade from M3/M4?"

VidLens finds 10+ reviews, reads the transcripts, extracts benchmark scores, and presents comparison charts — all from one prompt.

"I want to understand how AI agents work. Search YouTube for the best videos for a beginner and summarize what I need to know."

Discovers videos across creators, ranks by learning value, and prepares transcripts for follow-up questions.

"Search YouTube for reviews comparing the iPhone 17 Pro vs Samsung S26 Ultra. What do reviewers agree on? Where do they disagree?"

Searches, reads transcripts from multiple reviewers, and synthesizes consensus vs disagreements with sources.


🎯 Core Capabilities

🔍 Explore — One Prompt, Full Pipeline

Ask a question about YouTube and VidLens does the rest: searches, ranks by creator match and freshness, reads transcripts, extracts benchmark data, and presents comparison charts automatically. Works for product research, learning, competitive analysis — anything on YouTube.

🔎 Semantic Search Across Playlists

Import entire playlists or video sets, index every transcript with Gemini embeddings, and search across hundreds of hours of content by meaning — not just keywords.

👁️ Visual Search — See What's In Videos

Extract keyframes, describe them with Gemini Vision, run OCR on slides and whiteboards, and search by what you see — not just what's said.

📊 Intelligence Layer — Not Just Data

Sentiment analysis, niche trend discovery, content gap detection, hook pattern analysis, upload timing recommendations. The LLM does the thinking — VidLens gives it the right data.

⚡ Zero Config, Always Works

No API key needed to start. Three-tier fallback chain on every tool. Nothing breaks when quota runs out. Keys are optional power-ups.

🎬 Full Media Pipeline

Download videos/audio/thumbnails. Extract keyframes. Index comments for semantic search. Build a local knowledge base from any YouTube content.


⚡ Why VidLens?

VidLensOther YouTube MCP servers
🔑 Setup✅ Works immediately - no keys needed❌ Most require YouTube API key upfront
🛡️ Reliability✅ Three-tier fallback on every tool❌ Single point of failure - API down = broken
🧠 Intelligence✅ Sentiment, trends, content gaps, hooks❌ Raw data dumps - you do the analysis
📦 Token efficiency✅ 75-87% smaller responses❌ Verbose JSON with thumbnails, etags, junk
🔬 Depth✅ 41 tools across 10 modules⚠️ 1-5 tools, mostly transcripts only
🖼️ Visual evidence✅ Returns actual frame paths + timestamps, not just text hits⚠️ Usually transcript-only or raw frame dumps
⚖️ Trademark✅ Compliant naming⚠️ Most violate YouTube trademark

🚀 Quick Start

1. Install

npx vidlens-mcp setup

This auto-detects your MCP clients (Claude Desktop, Claude Code), downloads yt-dlp if needed, and configures everything. No manual setup required.

2. Or configure manually

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "vidlens-mcp": {
      "command": "npx",
      "args": ["-y", "vidlens-mcp", "serve"]
    }
  }
}

Claude Code — add to ~/.claude/settings.json:

{
  "mcpServers": {
    "vidlens-mcp": {
      "command": "npx",
      "args": ["-y", "vidlens-mcp", "serve"]
    }
  }
}

3. Restart your MCP client

Fully quit and reopen Claude Desktop (⌘Q). Claude Code picks up changes automatically.

4. Try it

Start with "Search YouTube" to activate VidLens:

"Search YouTube for the top M5 Max MacBook Pro reviews and tell me if it's worth upgrading from M4."

"Search YouTube for the best videos about agentic AI for a beginner."

"Import this playlist and search across all videos for mentions of machine learning."

"Search this video's frames for the benchmark comparison chart."

"What's trending in the AI coding niche right now?"


🧰 Tools - 41 across 10 modules

🔍 Explore - YouTube Discovery & Research

The front door — one prompt, full pipeline

ToolWhat it does
exploreYouTubeIntent-aware search with multi-query ranking, parallel enrichment, transcript summaries, structured benchmark data, and background indexing. One call replaces 5-8 individual tool calls.

📺 Core - Video & Channel Intelligence

Always available, no API key needed

ToolWhat it does
findVideosSearch YouTube by query with metadata
inspectVideoDeep metadata - tags, engagement, language, category
inspectChannelChannel stats, description, recent uploads
listChannelCatalogBrowse a channel's full video library
readTranscriptFull transcript with timestamps and chapters
readCommentsTop comments with likes and engagement
expandPlaylistList all videos in any playlist

🔎 Knowledge Base - Semantic Search

Index transcripts and search across them with natural language

ToolWhat it does
importPlaylistIndex an entire playlist's transcripts
importVideosIndex specific videos by URL/ID
searchTranscriptsNatural language search across indexed content
listCollectionsBrowse your indexed collections
setActiveCollectionScope searches to one collection
clearActiveCollectionSearch across all collections
removeCollectionDelete a collection and its index

💬 Sentiment & Analysis

Understand what audiences think and feel

ToolWhat it does
measureAudienceSentimentComment sentiment with themes and risk signals
analyzeVideoSetCompare performance across multiple videos
analyzePlaylistPlaylist-level engagement analytics
buildVideoDossierComplete single-video deep analysis

🎯 Creator Intelligence

Insights for content strategy

ToolWhat it does
scoreHookPatternsAnalyze what makes video openings work
researchTagsAndTitlesTag and title optimization insights
compareShortsVsLongShort-form vs long-form performance
recommendUploadWindowsBest times to publish for engagement

📈 Discovery & Trends

Find what's working in any niche

ToolWhat it does
discoverNicheTrendsMomentum, saturation, content gaps in any topic
exploreNicheCompetitorsChannel landscape and top performers

🎬 Media Assets

Download and manage video files locally

ToolWhat it does
downloadAssetDownload video, audio, or thumbnails
listMediaAssetsBrowse stored media files
removeMediaAssetClean up downloaded assets
extractKeyframesExtract key frames from videos
mediaStoreHealthStorage usage and diagnostics

🖼️ Visual Search

Three-layer visual intelligence. Not transcript reuse.

ToolWhat it does
indexVisualContentExtract frames, run Apple Vision OCR + feature prints, Gemini frame descriptions, and Gemini semantic embeddings
searchVisualContentSearch visual frames using semantic embeddings + lexical matching. Returns actual image paths + timestamps as evidence
findSimilarFramesImage-to-image frame similarity using Apple Vision feature prints

Three layers, all real:

  1. Apple Vision feature prints — image-to-image similarity (find frames that look alike)
  2. Gemini 2.5 Flash frame descriptions — natural language scene understanding per frame
  3. Gemini semantic embeddings — 768-dim embedding retrieval over OCR + description text for true text→visual search

What you always get back: frame path on disk, timestamp, source video URL/title, match explanation, OCR text, visual description.

What is NOT happening: no transcript embeddings are reused for visual search. This is a separate visual index.

💭 Comment Knowledge Base

Index and semantically search YouTube comments

ToolWhat it does
importCommentsIndex a video's comments for search
searchCommentsNatural language search over comment corpus
listCommentCollectionsBrowse comment collections
setActiveCommentCollectionScope comment searches
clearActiveCommentCollectionSearch all comment collections
removeCommentCollectionDelete a comment collection

🏥 Diagnostics

Health checks and pre-flight validation

ToolWhat it does
checkSystemHealthFull system diagnostic report
checkImportReadinessValidate before importing content

🔑 API Keys (Optional)

VidLens works without any API keys. Add them to unlock more capabilities:

KeyWhat it unlocksFree?How to get it
YOUTUBE_API_KEYBetter metadata, comment API, search via YouTube API✅ Free tier (10,000 units/day)Google Cloud Console → APIs → Enable YouTube Data API v3 → Credentials → Create API Key
GEMINI_API_KEYHigher-quality embeddings for semantic search (768d vs 384d)✅ Free tierGoogle AI Studio → Get API Key

⚠️ These are separate keys from separate Google services. A Gemini key will NOT work for YouTube API calls and vice versa. Create them independently.

# Configure via setup wizard
npx vidlens-mcp setup --youtube-api-key YOUR_YOUTUBE_KEY --gemini-api-key YOUR_GEMINI_KEY

# Or via environment variables
export YOUTUBE_API_KEY=your_youtube_key
export GEMINI_API_KEY=your_gemini_key

💻 CLI

npx vidlens-mcp               # Start MCP server (stdio)
npx vidlens-mcp serve         # Start MCP server (explicit)
npx vidlens-mcp setup         # Auto-configure Claude Desktop + Claude Code
npx vidlens-mcp doctor        # Run diagnostics
npx vidlens-mcp version       # Print version
npx vidlens-mcp help          # Usage guide

Doctor - diagnose issues

npx vidlens-mcp doctor --no-live

Checks: Node.js version, yt-dlp availability, API key validation, data directory health, MCP client registration (Claude Desktop, Claude Code).


📱 Works Everywhere — Desktop, Cowork, Phone

VidLens works across the full Claude ecosystem. Set it up once, use it everywhere.

Claude Desktop — Chat

The classic experience. Ask a question, get charts and analysis inline. Best for interactive research sessions.

Claude Desktop — Cowork Projects (March 2026)

Create a persistent research project with VidLens connected. Claude remembers context across sessions — last week's competitive research informs this week's analysis. Set up scheduled tasks that run automatically:

"Every Monday, search YouTube for new AI agent framework videos and compare to last week's findings."

Claude Dispatch — From Your Phone (March 2026)

Trigger any VidLens research from the Claude mobile app. Ask from your phone, Claude Desktop runs the tools locally, results come back to your pocket:

"Run my competitive research project — what new M5 Max content dropped this weekend?"

Claude Code — Remote Control

Start a Claude Code session with claude --remote-control, then continue from any browser or your phone at claude.ai/code. Full tool access, full context.

Note: Your Mac must be awake with Claude Desktop open for Cowork, Dispatch, and scheduled tasks to execute.


🏗️ Architecture

System Overview

VidLens System Overview

How the Fallback Chain Works

Every tool that touches YouTube data uses the same resilience pattern:

VidLens Fallback Chain

Every response includes a provenance field telling you exactly which tier served the data and whether anything was partial. No silent degradation — you always know what happened.

Visual Search Pipeline

Visual search is not transcript reuse. It's a dedicated three-layer index:

VidLens Visual Search Pipeline

Three layers, all real:

  1. Apple Vision feature prints — image-to-image similarity (find frames that look alike)
  2. Gemini Vision frame descriptions — natural language scene understanding per frame
  3. Gemini semantic embeddings — 768-dim retrieval over OCR + description text

Data Storage

Everything lives in a single directory. No external databases, no Docker, no infrastructure.

VidLens Data Storage

One directory. Portable. Back it up by copying. Delete it to start fresh.


📋 Requirements

RequirementStatusNotes
Node.js ≥ 22RequiredUses node:sqlitenode --version to check
yt-dlpAuto-installedDownloaded automatically during npx vidlens-mcp setup
ffmpegOptionalNeeded for frame extraction and visual indexing
YouTube API keyOptionalUnlocks comments, better metadata
Gemini API keyOptionalUpgrades transcript embeddings and frame descriptions for visual search
macOS Apple VisionAutomatic on macOSPowers native OCR and image similarity for visual search

🔧 Troubleshooting

"Tool not found" in Claude Desktop

Fully quit Claude Desktop (⌘Q, not just close window) and reopen. MCP servers only load on startup.

"YOUTUBE_API_KEY not configured" warning

This is informational, not an error. VidLens works without it. Add a key only if you need comments/sentiment features.

"API_KEY_SERVICE_BLOCKED" error

Your API key has restrictions. Create a new unrestricted key in Google Cloud Console, or remove the API restriction from the existing key.

Gemini key doesn't work for YouTube API

These are separate services. You need a YouTube API key from Google Cloud Console AND a Gemini key from Google AI Studio. They are not interchangeable.

Build errors

npx vidlens-mcp doctor     # Run diagnostics
npx vidlens-mcp doctor --no-live  # Skip network checks

📄 License

MIT


GitHub · npm · Model Context Protocol

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