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Glama
luigimasango-dev

youtube-transcript-mcp

by luigimasango-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

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

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_transcriptA

Fetch the full transcript of a YouTube video as plain text.

Args:
    video_url_or_id: Full YouTube URL (watch/youtu.be/embed/shorts) or bare 11-char video ID.
    languages: Preferred language codes in priority order, e.g. ["en", "en-GB"]. Defaults to ["en"].
    include_timestamps: If true, prefix each line with a [mm:ss] timestamp.
get_video_metadataA

Fetch title, channel name, and thumbnail for a YouTube video via the no-auth oEmbed endpoint.

Note: oEmbed does not expose upload date or full description (those require the official
Data API + an API key). This returns what's available without one.

Args:
    video_url_or_id: Full YouTube URL or bare 11-char video ID.
search_transcriptA

Search a video's transcript for a keyword/phrase and return matching segments with timestamps.

Args:
    video_url_or_id: Full YouTube URL or bare 11-char video ID.
    query: Keyword or phrase to search for (case-insensitive substring match).
    languages: Preferred language codes in priority order. Defaults to ["en"].
summarize_chaptersA

Break a transcript into rough time-blocked chunks at natural pauses, for easier per-section summarizing.

This is a heuristic, not real chapter data: it splits wherever the gap between two consecutive
caption snippets exceeds `gap_seconds`, then merges any resulting chunk shorter than
`min_chunk_seconds` into its neighbor so chunks stay summarizable.

Args:
    video_url_or_id: Full YouTube URL or bare 11-char video ID.
    gap_seconds: Silence gap (seconds) between captions that triggers a new chunk boundary.
    min_chunk_seconds: Minimum chunk duration; shorter chunks get merged forward.
    languages: Preferred language codes in priority order. Defaults to ["en"].

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored across 4 tools

Disambiguation5/5

Each tool targets a distinct operation: fetching raw transcript text, fetching metadata, searching within a transcript, and chunking into chapters. Even though three tools operate on transcripts, their purposes are clearly separated and descriptions make the boundaries obvious.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: get_transcript, get_video_metadata, search_transcript, summarize_chapters. The verbs clearly indicate the action, and the nouns indicate the resource or output.

Tool Count5/5

With 4 tools, the server is well-scoped for a focused YouTube transcript utility. Each tool covers a meaningful, non-redundant capability without unnecessary bloat.

Completeness4/5

The set covers the core transcript workflows: fetching, searching, chunking, and basic metadata retrieval. A minor gap is the lack of a tool to list available transcript languages or caption tracks, which could help when the default language is unavailable.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources