EarScribe MCP Server

MCP server for EarScribe

A Model Context Protocol server that exposes the canonical EarScribe knowledge surface — local-first AI workflows, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://earscribe.app

🖥️ About EarScribe

EarScribe is a browser-based transcription tool that converts audio files into text entirely on the user's device. Powered by OpenAI's Whisper model running locally via WebGPU or WebAssembly, it produces timestamped transcripts and subtitle files without sending any audio to external servers. There is no account to create, no subscription to manage, and no per-minute fee — the tool works in the browser, caches the Whisper model locally after the first load, and can operate offline from that point forward. It supports a wide range of audio formats and handles recordings up to approximately two hours long, making it a practical option for anyone who regularly works with spoken audio.

Key Features

• Private by design — audio files never leave the user's device; all processing happens locally in the browser using WebGPU when available, falling back to WebAssembly automatically.
• Multiple export formats — transcripts can be downloaded as SRT, VTT, JSON, or plain text, covering both subtitle workflows and raw text needs.
• Automatic language detection — Whisper identifies the spoken language from the audio without manual configuration, supporting 99 languages.
• Selectable model sizes — users can choose from Whisper model sizes ranging from Tiny to Turbo depending on the balance they want between speed and accuracy, with each model cached locally after download.
• Timestamped output — every transcript segment is tied to a time position synchronized with a waveform display, making it straightforward to locate specific moments in the original recording.
• In-browser editing — transcripts can be reviewed and adjusted directly in the interface before export, removing the need to copy text into a separate editor for minor corrections.

Use Cases

• Podcast production — generate searchable episode transcripts or show notes from recorded conversations without uploading sensitive pre-release content to a third-party service.
• Journalism and research interviews — transcribe source recordings that may contain confidential material, keeping the audio on a local machine throughout the process.
• Academic study — convert lecture recordings into readable, searchable text for review and note-taking.
• UX and user research — transcribe session recordings and user interviews for analysis, with no risk of research data passing through external infrastructure.
• Video subtitle creation — export SRT or VTT files directly from a recording to bring into video editing software, skipping manual captioning or cloud captioning services.

Who Is It For

EarScribe is well-suited for anyone who transcribes audio regularly and either has privacy concerns about uploading recordings to cloud services or wants to avoid ongoing subscription costs. Journalists, academic researchers, and UX professionals dealing with confidential or sensitive interviews will find the local processing model particularly useful. Podcasters and v

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