In-depth architectural comparison of the Conversion Tools and Whisper Windows MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Conversion Tools
Speech-to-Text · Local stdio
Quality: 25/100 (Emerging) | Auth: No auth required
Whisper Windows MCP
Speech-to-Text · Local stdio
Quality: 64/100 (Good) | Auth: No auth required
Verdict Summary: Choose Conversion Tools if you need specialized Speech-to-Text tools running via a local process. Choose Whisper Windows MCP if your workspace requires Speech-to-Text integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
C
Choose Conversion Tools when:
You need dedicated capabilities in the Speech-to-Text domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Convert files too big or exotic for a sandbox: 140+ formats, batch, OCR, AI extraction, TTS/STT
Windows-native local audio and video transcription using whisper.cpp with Vulkan GPU acceleration. No cloud APIs, no Python. Batch processing, multilingual support, model management, and background job handling built in.
No explicit tool names declared in metadata yet. Check project README on main listing page.
Whisper Windows MCP Tools (12)
transcribe_audio
Transcribe a single file. Supports blocking (default) or background mode for long files.
check_progress
Monitor a background transcription job started with `transcribe_audio` (background=true).
start_batch
Automated sequential batch transcription of all untranscribed files in a folder. Sorts by duration (shortest first), processes one at a time as background jobs, validates each output. Batch self-advances when each file finishes — no polling required.
check_batch_progress
Monitor a running batch. Automatically advances to the next file when the current one finishes. Returns overall progress, current file with timestamp, and any failed files.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Conversion Tools is categorized under Speech-to-Text and uses a local stdio subprocess. In contrast, Whisper Windows MCP belongs to Speech-to-Text using local stdio subprocess. Select Conversion Tools when you need capabilities focused on speech-to-text and Whisper Windows MCP when you require tools for speech-to-text.
Generate subtitle files. Supports automatic language detection and English translation output. Outputs SRT (widest compatibility) or WebVTT (web and HTML5 video).
analyze_media
Analyze files before committing to transcription. Returns duration, size, codec, and estimated transcription time on CPU and GPU. For folders, shows all files in a sortable table with transcription status.
check_config
Verify whisper-cli.exe, the model file, and FFmpeg are all accessible. Run this first if anything is failing.
list_models
List all Whisper model files installed in your models directory. Shows filename, size, whether it is currently active, quantization status, and recommended use case. No network calls — reads local filesystem only.
download_model
Download a Whisper model directly from Hugging Face into your models directory. Only downloads from trusted Hugging Face namespaces. After downloading, use `switch_model` to activate it.
switch_model
Switch the active Whisper model for the current session without restarting Claude Desktop. Change is session-scoped — does not persist after restart. To make permanent, update `WHISPER_MODEL` in your config.
check_system
Detect GPU hardware and verify Vulkan acceleration is available. Reports GPU name, VRAM, whether `ggml-vulkan.dll` is present, and recommends the best model size for your hardware.
whisper_server
Start, stop, or check the **persistent model server** (whisper.cpp's `whisper-server`). While running, the active model stays resident in VRAM and every `transcribe_audio` / `transcribe_batch` call is served over localhost with **no per-file model reload** — a large speedup when transcribing many s…