YouTube Research MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| youtube_searchA | Search YouTube for videos matching a query without needing an API key. Returns a structured list of videos with IDs, titles, channels, durations, views, and URLs. Use this tool when you need to discover videos on a topic or find candidate videos for research. |
| youtube_videoA | Retrieve complete metadata, view statistics, tags, chapters, and caption availability for a specific YouTube video. Use this tool to inspect a video's table of contents (chapters) and determine if spoken transcripts are available. |
| youtube_transcriptA | Extract the spoken transcript of a YouTube video with timestamped segments and language provenance. Returns requested_language, actual_language, and fallback_used flags. Never silently swaps languages unless fallback_language is specified. |
| youtube_find_in_videoA | Pinpoint exact sections and timestamps in a long video where a specific topic or concept is discussed. Uses in-process Hybrid RRF (FastEmbed ONNX dense vectors + BM25 lexical search) to locate the most relevant 2-3 minute chunks. Returns deep-link timestamp URLs (e.g. ?t=842s), relevance scores, chapter context, and exact spoken quotes. PREFERRED over reading full transcripts for videos longer than 10 minutes. |
| youtube_researchA | Autonomous multi-video research tool. Discovers relevant YouTube videos across diverse channels, extracts spoken transcripts concurrently, performs semantic search, and aggregates timestamped citations with near-duplicate claim clustering. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| get_health_resource | Returns real-time provider health, circuit breaker states, and metrics telemetry. |
TDQS
Scored across 5 tools
The tools mostly target distinct actions: metadata retrieval, search, transcript extraction, pinpointing content, and autonomous research. The only potential confusion is between youtube_search (finding videos) and youtube_research (which also discovers videos), but the descriptions clarify that youtube_research is a higher-level aggregation tool that subsumes search and transcript extraction.
All tools share the 'youtube_' prefix, which is consistent, but the suffixes mix nouns (video, transcript, research) with verbs (search, find_in_video). This is not a strict verb_noun pattern like the calibration example. The inconsistency is minor but noticeable; a more uniform pattern like youtube_get_video, youtube_search_videos, etc., would improve coherence.
With 5 tools, the count is well within the ideal 3-15 range. Each tool serves a distinct purpose in the research workflow (discovery, metadata, transcript, semantic search, aggregation), and none feels redundant. The number is appropriate for the server's stated purpose.
The tool surface covers the core research lifecycle: search, inspect (metadata), extract (transcript), locate (find_in_video), and synthesize (research). Minor gaps exist, such as no tool for channel-specific queries or playlist retrieval, but these are not essential for the primary research workflow. The inclusion of caption availability checks and language provenance shows attention to detail.