Wwise-MCP
Reduces audio production cycle time by enabling LLMs to automate repetitive Wwise workflows, freeing sound engineers to focus on creative work rather than ma…
Wwise-MCP is a Model Context Protocol (MCP) server that enables large language models (LLMs) to interact with the Wwise Authoring application. It exposes a set of tools built on a custom Python WAAPI library, allowing MCP clients such as Claude or Cursor to automate and compose complex, multi-step Wwise workflows.
- Ask Claude to automate repetitive audio project setup tasks in Wwise Authoring.
- Generate sound event configurations across multiple Wwise projects without manual clicking.
- Find and modify audio object properties in bulk within your Wwise application.
Reduces audio production cycle time by enabling LLMs to automate repetitive Wwise workflows, freeing sound engineers to focus on creative work rather than manual task execution.
Audio engineering teams using Wwise seeking to automate project composition and multi-step audio workflows via Claude or Cursor.
https://github.com/BilkentAudio/Wwise-MCP
By BilkentAudio
How to Get It
See repository README
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Automate repetitive audio project setup tasks in Wwise Authoring
Trust Signals Auto-scanned
Data & Access
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 45 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 45 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.