Analyze YouTube channels to extract structured intelligence reports.
Analyze a YouTube channel by URL, @handle, channel ID, or legacy /c/ and /user/ URLs.
Fetch transcripts from recent videos (up to 50, default 5).
Extract semantic topics per video with Gemini: theme, named entities, and tags (when GEMINI_API_KEY is set).
Fall back to keyword-frequency topic extraction across all transcripts when Gemini is unavailable.
Return structured JSON with channel metadata, video IDs, transcript availability counts, topics, and per-video semantic analysis.
Optionally write per-channel JSON artifacts to a local output directory (ANALYZE_CHANNEL_OUTPUT_DIR).
Works as an MCP server for Claude Desktop or compatible MCP clients.
Extracts structured intelligence from YouTube channels, including transcript extraction, topic frequency analysis, competitive signal detection, and content positioning analysis.
MCP server for extracting structured intelligence from YouTube channels and videos.
Analyzes YouTube channels to produce structured intelligence reports:
Transcript extraction across recent videos (up to 50 videos)
Semantic topic extraction per video via Gemini (theme, named entities, tags)
Keyword frequency analysis across all transcripts (fallback when Gemini is unavailable)
Related MCP server: MCP YouTube Intelligence
You need API keys for three services:
Variable | Where to get it |
| Google Cloud Console → YouTube Data API v3 |
| Apify Console → Account → Integrations → API token |
| Google AI Studio → Get API key |
GEMINI_API_KEY is optional — if omitted, the tool falls back to word-frequency topic extraction instead of semantic analysis.
Optional
Variable | Default | Description |
|
| Directory where per-channel JSON analysis artifacts are written |
npm install -g mcp-youtube-intelligenceAdd to your Claude Desktop / MCP client config:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"youtube-intelligence": {
"command": "mcp-youtube-intelligence",
"env": {
"YOUTUBE_API_KEY": "your-youtube-api-key",
"APIFY_TOKEN": "your-apify-token",
"GEMINI_API_KEY": "your-gemini-api-key"
}
}
}
}analyze_channel — Extract intelligence from a YouTube channel
channel_url: YouTube channel URL or @handle (e.g. @fireship, youtube.com/@fireship)
max_videos: Number of recent videos to analyze (default: 5, max: 50)Example prompt: "Analyze the @fireship YouTube channel and tell me what topics they cover most."
npm install
npm run build
npm testMIT
Analyze a YouTube channel and return a JSON object with: channel_id, channel_title, channel_url, sample_video_ids[], videos_analyzed (count of videos fetched from playlist), transcripts_available (count with actual caption content), topics[] (top keyword frequencies across all transcripts), topics_structured[] (per-video semantic analysis — each entry has video_id/theme/entities[]/tags[]), note (which analysis mode ran), and optional output_path (local artifact path). Requires YOUTUBE_API_KEY and APIFY_TOKEN; set GEMINI_API_KEY for topics_structured semantic analysis (falls back to keyword-only when absent). Supported channel inputs: @handle (e.g. @fireship), youtube.com/@handle URL, /channel/UC... URL, bare 24-char UCxxxxxx ID, or legacy /c/ and /user/ URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| max_videos | No | Number of recent videos to analyze (default 5, max 50) | |
| channel_url | Yes | YouTube channel URL or @handle (e.g. @fireship, https://www.youtube.com/@fireship, UCxxxxxxx) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses output structure, dependencies, fallback mode, and input types. Missing details on rate limits or error handling, but still transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose but every sentence adds useful information. It front-loads the output structure and maintains clarity. Could be slightly trimmed without losing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description fully explains the return object. Covers inputs, dependencies, and analysis modes, making it complete for a tool with two parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds value by explaining accepted formats for channel_url and notes max_videos default/limit. Minor overlap with schema for max_videos.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: analyze a YouTube channel and return a JSON object with specific fields. It distinguishes the resource and action without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context on when to use, including required API keys, fallback behavior, and supported input formats. However, it does not explicitly mention when not to use or alternatives, though no siblings exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.10analyze_channelOnly one tool exists, so there is no possibility of confusion with other tools.
The single tool name 'analyze_channel' follows a clear verb_noun pattern, making it predictable and unambiguous.
With only one tool for a domain like YouTube intelligence, the surface is too narrow; typical well-scoped servers have 3-15 tools.
The server only offers channel analysis, missing obvious operations like video search, video details, playlist management, and subscription handling, leaving significant gaps.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
An MCP server that gives any LLM or agent clean YouTube transcripts on demand: a single video, a whole channel, or a playlist, plus AI cleanup of auto-generated captions. API-key auth, credit-based, same backend as the public v1 API. Get a free API key with 25 free credits at youtubetranscriptdownload.com/account.
💯 The fastest YouTube transcript + YouTube search MCP for AI agents. Try for free.
YouTube transcripts, search, channel/playlist listings and upload tracking for AI agents. No signup.
Multimodal video analysis MCP — transcription, vision, and OCR for any video URL.
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