YTPipe

Transforms YouTube videos into LLM-ready knowledge bases with a production-ready MCP backend.

python

0

GitHub Stars

python

Language

4 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "leolech14-ytpipe": {
      "command": "python",
      "args": [
        "-m",
        "ytpipe.mcp.server"
      ]
    }
  }
}

YTPipe provides an MCP server that exposes AI-assisted video processing capabilities as callable tools. It enables you to transform YouTube videos into knowledge bases by running a production-ready backend that coordinates transcription, chunking, embeddings, and search, all behind a scalable MCP interface so you can build agents and workflows around YouTube content.

How to use

You will run the MCP server locally or in your environment and connect an MCP client to issue AI-driven actions. Start by launching the MCP server using the Python-based command shown in your environment, then interact with it through your client or CLI to process videos, run searches, or optimize SEO.”},{

Available tools

ytpipe_process_video

Executes the full pipeline from download to vector storage, returning metadata, transcript, chunks, and embeddings.

ytpipe_download

Downloads video assets or audio for processing.

ytpipe_transcribe

Converts audio to transcript using speech-to-text.

ytpipe_embed

Generates text embeddings for semantic search.

ytpipe_search

Performs full-text search over transcripts and chunks.

ytpipe_find_similar

Performs semantic similarity search across chunks.

ytpipe_get_chunk

Retrieves a specific chunk by its ID.

ytpipe_get_metadata

Fetches video metadata such as title, description, and duration.

ytpipe_seo_optimize

Provides SEO recommendations for video title, tags, and description.

ytpipe_quality_report

Generates quality metrics for the processing results.

ytpipe_topic_timeline

Analyzes topic evolution and keyword density over time.

ytpipe_benchmark

Runs performance analyses on the processing pipeline.