io.github.AudialAI/audial-mcp
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Alternatives to io.github.AudialAI/audial-mcp
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AlicenseNot gradedqualityDmaintenanceProvides ten hosted audio AI tools — TTS, voice cloning, music generation, stem separation, speaker separation, transcription, denoising, media conversion, and job polling — over a single streamable-HTTP endpoint for any MCP-capable agent.MIT- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to programmatically edit, analyze, and export audio projects through MCP tools, including multi-track editing, effects, transcription, and semantic search.1-
- FlicenseNot gradedqualityBmaintenanceEnables AI clients like Claude and ChatGPT to generate images and videos, animate images, create lip-synced videos, list TTS voices, and manage media via remote MCP tools.-
- AlicenseBqualityBmaintenanceEnables MCP-compatible agents to queue, run, and track local AI music generation jobs with dry-run defaults, license recording, and pluggable pipeline adapters for YuE or compatible backends.31AGPL 3.0
- AlicenseAqualityCmaintenanceEnables AI agents to master audio tracks to target LUFS/True Peak levels, remove Suno/Udio AI fingerprints, and retrieve mastering passports via a hosted MCP server.1133 npm1MIT
- AlicenseNot gradedqualityAmaintenanceEnables agents to access OpenAI-compatible transcription, live ASR, text-to-speech, voice cloning, phoneme recognition, and server-side file staging through streamable HTTP MCP tools.5Do What The F*ck You Want To Public
TDQS
Scored across 10 tools
Most tools cover distinct audio operations such as stem splitting, analysis, mastering, MIDI transcription, and vocal synthesis, so many choices are clear. However, generate_music advertises stem extraction, analysis, and completion, overlapping with stem_split, analyze, and segment, which could cause misselection.
Names follow mixed conventions: verb-only (analyze, segment, master), verb_noun (generate_samples, list_results), and concatenated forms (stem_split, sound2vital, text2vox). They remain readable, but there is no predictable naming pattern.
With 10 tools, the set sits in the recommended 3-15 range and each tool represents a distinct audio or music capability. The surface does not feel bloated or thin for this domain.
Core workflows are covered: splitting, analysis, segmentation, mastering, sample/MIDI generation, music/vocal generation, synth preset conversion, and listing results. A minor gap is the lack of get_result/status or deletion/management operations beyond list_results.