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go_mcp_server_youtube_search

go_mcp_server_youtube_search

Local
github.com/hightempMedia & Design4Updated 74w ago

About go_mcp_server_youtube_search

go_mcp_server_youtube_search is an MCP server for AI assistants that need to search YouTube and retrieve video details or subtitles.

Key features

  1. 01Search YouTube by text query
  2. 02Get video details by ID
  3. 03Retrieve English or Russian subtitles
  4. 04Support SSE and stdio modes

Use cases

  1. 01Find videos on a specific topic
  2. 02Check video details and subtitles
  3. 03Call YouTube search from an AI assistant

MCP support

The MCP server exposes tools for YouTube search, video information, and subtitles. It supports SSE and stdio transports.

Configuration

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

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

go_mcp_server_youtube_search

A simple MCP (Model Context Protocol) server that provides tools for searching and retrieving information from YouTube videos. This server can be used with AI assistants that support the MCP protocol.

Server url: http://localhost:8889/sse

Tools

  • youtube_search - Search for YouTube videos using a text query
  • youtube_get_video_info - Get detailed information about a video by its ID
  • youtube_get_subtitles - Get video subtitles in English or Russian

Usage

The server can operate in two modes: stdio and sse (Server-Sent Events). By default, it uses the sse mode.

go_mcp_server_youtube_search -t sse -h 0.0.0.0 -p 8889
# or
go_mcp_server_youtube_search -t stdio

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.