Subtitle MCP Server

Overview

The Subtitle MCP Server is a dedicated, local service that equips AI assistants with advanced subtitle‑centric capabilities. It solves the common bottleneck of accessing, manipulating, and deriving insights from video content by offering a single, cohesive API surface that handles everything from raw subtitle retrieval to high‑level comprehension assessment. Developers building AI workflows that involve media consumption, language learning, or content curation can now outsource the heavy lifting of subtitle processing to this server instead of reinventing transcription pipelines or parsing files manually.

At its core, the server exposes four principal service groups: Subtitle Management, Summarization & Highlighting, Transcription, and YouTube Extraction. The subtitle management module reads files from a configurable directory and serves them by filename, allowing downstream tools to query specific videos without file‑system traversal. Summarization and highlighting turn raw subtitle text into concise summaries, flagging pivotal moments or key dialogue. The same module also generates simple comprehension quizzes that can be used in educational settings or content quality checks.

Transcription is powered by state‑of‑the‑art Automatic Speech Recognition models (Whisper), enabling the server to convert local audio and video files into fully timestamped subtitle files. This eliminates the need for external transcription services or manual captioning, ensuring consistent format and quality across media types. The YouTube extraction feature extends the server’s reach to online content: it downloads official subtitles when available, or falls back to on‑the‑fly transcription of the video stream. This is especially useful for researchers or content creators who need to quickly process large numbers of YouTube videos.

In practice, a developer can integrate the Subtitle MCP Server into an AI assistant that supports reading comprehension. For example, a language‑learning chatbot could request a summary of a news video, receive highlighted key points, and then generate quiz questions—all through simple MCP calls. Similarly, a media analytics platform could batch‑process thousands of video files to extract subtitles, summarize them, and store the results in a database for downstream analytics. The server’s local deployment guarantees low latency and privacy, as no external network traffic is required for transcription or summarization.

Unique advantages of this MCP include its all‑in‑one approach (managing, summarizing, transcribing, and extracting) and its modular architecture that allows developers to enable or disable features as needed. By abstracting away the complexities of speech‑to‑text, subtitle parsing, and content analysis, it lets AI developers focus on higher‑level logic—such as intent understanding or dialogue generation—while still delivering rich, media‑aware interactions.