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YouTube Transcript MCP Server

by suckerfish
GitHub
Python
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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

# Install dependencies uv pip install -e . # Run server (STDIO mode) python src/server.py # Run server (HTTP mode) uvicorn src.server:app --host 0.0.0.0 --port 8080

Docker (Recommended)

# Build and run docker build -t yttranscript-mcp . docker run -d -p 8080:8080 yttranscript-mcp # Or use docker-compose docker-compose up -d yttranscript-mcp # Health check curl http://localhost:8080/health

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

# Discover tools mcp tools .venv/bin/python src/server.py # Basic transcript mcp call get_transcript --params '{"video_id":"jNQXAC9IVRw"}' .venv/bin/python src/server.py # Time-filtered transcript mcp call get_transcript --params '{"video_id":"jNQXAC9IVRw", "start_time": 10, "end_time": 60}' .venv/bin/python src/server.py # Search within transcript mcp call search_transcript --params '{"video_id":"jNQXAC9IVRw", "query":"example"}' .venv/bin/python src/server.py # Advanced analytics mcp call get_transcript_summary --params '{"video_id":"jNQXAC9IVRw"}' .venv/bin/python src/server.py # Available languages mcp call get_available_languages --params '{"video_id":"jNQXAC9IVRw"}' .venv/bin/python src/server.py

MCP Client Configuration

HTTP Transport (Production)

{ "yttranscript": { "command": "uvicorn", "args": [ "src.server:app", "--host", "0.0.0.0", "--port", "8080" ], "cwd": "/path/to/yttranscript_mcp" } }

STDIO Transport (Development)

{ "yttranscript": { "command": "uv", "args": [ "run", "--directory", "/path/to/yttranscript_mcp", "src/server.py" ] } }

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

YT_TRANSCRIPT_SERVER_PORT=8080 # Server port (default: 8080) YT_TRANSCRIPT_SERVER_HOST=0.0.0.0 # Server host (default: 0.0.0.0) YT_TRANSCRIPT_DEBUG=false # Debug mode

Docker Environment

# Production docker run -e YT_TRANSCRIPT_SERVER_PORT=8080 yttranscript-mcp # Development with auto-reload docker-compose --profile dev up yttranscript-mcp-dev

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.

This server cannot be installed

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security - not tested
F
license - not found
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quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables fetching, searching, and analyzing YouTube video transcripts in multiple languages using yt-dlp. Supports timestamp filtering, language detection, and transcript summaries with robust error handling for production use.

  1. Status: Production Ready ✅
    1. Features
      1. Installation
        1. Quick Start
        2. Docker (Recommended)
      2. Usage
        1. Available Tools
        2. Testing Commands
      3. MCP Client Configuration
        1. HTTP Transport (Production)
        2. STDIO Transport (Development)
      4. Key Features
        1. Universal Parameter Compatibility
        2. Advanced Analytics
        3. CLI Implementation Benefits
      5. Configuration
        1. Environment Variables
        2. Docker Environment
      6. Dependencies
        1. Troubleshooting
          1. License

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