youtube-mcp-server
Provides an MCP server over SSE that lets AI agents extract YouTube video metadata (via yt-dlp) and generate audio transcriptions using VAD (Silero VAD) and Whisper, with caching and configurable performance settings.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local-only SSE is implied, but TLS is not documented. No auth or scopes are described, so access control likely depends entirely on network isolation. This tool downloads/uses external resources (YouTube via yt-dlp; model downloads via dependencies), which may expand your threat surface. No details are provided on secret handling, logging, or safe filesystem permissions for caching/transcripts.
⚡ Reliability
Best When
You want a locally run MCP tool for YouTube transcription/metadata that can be called programmatically by an agent, and you can install ffmpeg plus the required Python dependencies/models.
Avoid When
You need strict production-grade guarantees (documented SLAs, explicit retry/idempotency semantics, and well-specified error contracts) or you cannot run external binaries/models (ffmpeg, Whisper weights, Silero models).
Use Cases
- • Agent-assisted research: fetch title/description/duration/tags/thumbnails for YouTube videos
- • Meeting/interview analysis: transcribe long-form videos into timestamped segments
- • Multilingual transcription and translation for content pipelines
- • Automated content indexing: store transcripts + metadata in a local cache for retrieval
Not For
- • Use where you need OAuth-protected access control or per-user authorization
- • Highly sensitive environments that prohibit third-party external network calls to YouTube or model downloads
- • Applications requiring strong, documented rate-limiting guarantees and usage quotas
Interface
Authentication
README shows local startup and MCP client configuration with only the URL; no authentication/authorization mechanism is described.
Pricing
No SaaS pricing mentioned; the workload depends on the selected Whisper model and local compute resources.
Agent Metadata
Known Gotchas
- ⚠ Long-running operations: transcription can be slow for lengthy videos; agents may need timeouts/async handling.
- ⚠ External dependencies: requires ffmpeg plus model downloads (Whisper weights, Silero VAD).
- ⚠ Concurrency tuning: MAX_WORKERS may increase CPU/RAM usage and could trigger failures under constrained environments.
- ⚠ Rate limits: no rate-limit documentation is provided; YouTube/yt-dlp access may be subject to external throttling.
- ⚠ Caching behavior: transcript cache location is configurable but cache invalidation/versioning strategy is not described.
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Scores are editorial opinions as of 2026-03-30.