mcp-youtube-intelligence

Provides an MCP server (stdio-based) and CLI to extract YouTube video metadata/transcripts, generate token-optimized summaries, perform topic segmentation and entity extraction, analyze comments with sentiment, and support channel monitoring, playlist analysis, search, and batch operations. Optional LLM providers (local via Ollama/vLLM/LM Studio, or cloud via OpenAI/Anthropic/Google) power the summarization/enrichment steps with a fallback to extractive summarization.

Evaluated Mar 30, 2026 (22d ago)
Repo ↗ Ai Ml mcp youtube transcripts summarization entity-extraction topic-segmentation comment-sentiment python cli
⚙ Agent Friendliness
65
/ 100
Can an agent use this?
🔒 Security
35
/ 100
Is it safe for agents?
⚡ Reliability
32
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
82
Documentation
78
Error Messages
0
Auth Simplicity
75
Rate Limits
20

🔒 Security

TLS Enforcement
20
Auth Strength
35
Scope Granularity
10
Dep. Hygiene
55
Secret Handling
60

Communication with the MCP server is described as stdio (local), but no security controls (authn/authz, transport/TLS for any network calls, or input hardening) are documented. LLM provider credentials are configured via environment variables. The dependency list includes common libraries (yt-dlp, feedparser, aiosqlite); no explicit vulnerability management or pinning/CVE posture is provided in the provided materials.

⚡ Reliability

Uptime/SLA
0
Version Stability
35
Breaking Changes
40
Error Recovery
55
AF Security Reliability

Best When

You want structured YouTube intelligence with an agent-friendly MCP interface and you can run local or provide cloud LLM credentials, while benefiting from server-side transcript processing to reduce LLM token usage.

Avoid When

You need well-documented operational guarantees (SLA, pagination semantics, strict rate-limit contracts) or you cannot supply/handle LLM credentials for the enrichment features.

Use Cases

  • Generate concise summaries from YouTube transcripts for LLM context constraints
  • Extract entities and topics from videos for downstream knowledge extraction
  • Analyze viewer comments for sentiment and reaction themes
  • Monitor a channel for new activity via RSS-style checks
  • Search within previously stored transcripts
  • Batch processing of multiple video IDs for intelligence workflows

Not For

  • Real-time guarantees or low-latency production streaming use cases
  • Use where YouTube transcripts must be retrieved under strict contractual/availability constraints without fallbacks
  • Security-sensitive environments requiring formally specified threat models and audit artifacts for this exact package

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
No
Webhooks
No

Authentication

Methods: Optional cloud LLM API keys via environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY) Optional local LLM endpoints (Ollama/vLLM/LM Studio) configured via environment variables
OAuth: No Scopes: No

The MCP/CLI itself is described as locally configured via env vars. No user-facing OAuth scopes or auth for accessing the MCP server are documented in the README content provided.

Pricing

Free tier: No
Requires CC: No

No pricing tiers are described; only the optional LLM providers imply cost.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • LLM-based summarization may fail; documentation states an extractive fallback is used, but details of error recovery/retry are not specified.
  • YouTube URLs should be quoted in zsh due to '?' character handling (CLI gotcha).
  • Requires yt-dlp availability for transcript extraction; missing yt-dlp can cause failures.
  • For MCP via stdio, agents must launch the MCP server using the provided command (e.g., uvx) and pass required environment variables.

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Scores are editorial opinions as of 2026-03-30.

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