mcpField
Y
youtube-transcripts

youtube-transcripts

Local
apify.comSocial Media
RepositoryVisit website

About youtube-transcripts

YouTube transcripts for AI agents: videos, channels, playlists, search. Timestamps, SRT, VTT

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "fetchworks-dev-youtube-transcripts": {
      "command": "npx",
      "args": [
        "-y",
        "fetchworks-mcp"
      ],
      "env": {
        "APIFY_TOKEN": "<APIFY_TOKEN>"
      }
    }
  }
}
Run commandnpx -y fetchworks-mcp
Environment variables to set
APIFY_TOKENSecretApify API token (free at console.apify.com/settings/integrations). Transcript extraction is billed to your Apify account at $2 per 1,000 delivered transcripts.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmfetchworks-mcp0.1.1

Tools

3

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
videosarrayYesYouTube video URLs or bare 11-character video IDs. One transcript per video.
languagesarrayNoLanguage priority list (ISO codes, e.g. ["en", "de"]). First available caption track wins. Default ["en"].
translateTostringNoTarget language code (e.g. "es") for YouTube caption auto-translation. Best-effort.
outputFormatsarrayNoTranscript representations to include. Default ["text"] for compact tool output.
includeChaptersbooleanNoInclude video chapters. Default false.
includeMetadatabooleanNoInclude title, channel, duration, views, publish date. Default true.
preferAutoGeneratedbooleanNoPrefer auto-generated (ASR) captions over manual ones. Default false.

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.