YouTube Transcript MCP Server

A production-ready Model Context Protocol (MCP) server that provides YouTube transcript fetching capabilities using yt-dlp CLI for reliable subtitle extraction. Bypasses YouTube's rate limiting through CLI-based implementation.

Status: Production Ready ✅

Implementation: Full CLI migration complete (September 2025)

• ✅ CLI-Based: Uses yt-dlp subprocess to avoid HTTP rate limiting

• ✅ Universal Compatibility: Time parameters work across all MCP clients

• ✅ Advanced Analytics: Enhanced transcript summary with content analysis

• ✅ Multi-Language: 100+ languages with auto-generated and manual transcripts

Features

• Fetch transcripts from YouTube videos with metadata and timestamps

• Time filtering - extract specific segments by start/end times

• Search functionality - find text within transcripts with context

• Advanced analytics - speaking pace, filler words, engagement metrics, top words

• Language detection - list available transcript languages

• Universal format support - handles both video IDs and full YouTube URLs

• Dual transport - STDIO and HTTP transport modes

• Docker support - containerized deployment with health checks

Installation

Quick Start

Docker (Recommended)

Usage

Available Tools

1. get_transcript - Fetch video transcripts with optional time filtering

2. search_transcript - Search for specific text within transcripts

3. get_transcript_summary - Advanced analytics and content insights

4. get_available_languages - List available transcript languages

Testing Commands

MCP Client Configuration

HTTP Transport (Production)

STDIO Transport (Development)

Key Features

Universal Parameter Compatibility

Time filtering parameters accept multiple formats:

• Integers: {"start_time": 10}

• Floats: {"start_time": 10.5}

• Strings: {"start_time": "10"}

• Nulls: {"start_time": null} or {"start_time": "null"}

Advanced Analytics

The get_transcript_summary tool provides:

• Speaking pace analysis (words per minute with descriptive labels)

• Filler word detection (um, uh, like, etc.) with percentages

• Content indicators (conversational, formal, high energy)

• Top frequent words (excluding stop words)

• Engagement metrics (questions, exclamations)

• Reading time estimates at multiple speeds

CLI Implementation Benefits

• No rate limiting - bypasses YouTube's HTTP restrictions

• Reliable extraction - uses yt-dlp's robust parsing

• Better error handling - clear error messages for various failure modes

• Format flexibility - handles VTT, JSON3, and other subtitle formats

Configuration

Environment Variables

Docker Environment

Dependencies

• fastmcp>=0.9.0 - MCP server framework

• yt-dlp>=2025.8.11 - YouTube transcript extraction via CLI

• pydantic>=2.0.0 - Data validation and models

• uvicorn>=0.24.0 - ASGI server for HTTP transport

This project uses uv for package management.

Troubleshooting

• Tool not found: Verify @mcp.tool() decorator in tool definitions

• Validation errors: Video IDs must be 11 characters, time values must be non-negative

• Time filtering issues: Parameters accept multiple formats (int/float/string/null)

• Transport issues: Use uvicorn for HTTP mode, python src/server.py for STDIO

• No transcript available: Check with get_available_languages first

License

This project is open source and available under the MIT License.