Wwise-MCP

Name: Wwise-MCP Review
Item: Wwise-MCP
Rating: 2.8
Author: Aaron Matthews

Connector Productivity Usable

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.

45 starsApache-2.0 (commercial OK)Free

Fair rating — This tool is functional but has notable gaps. Review the evaluation notes below before deploying.

Works in

Claude Code Claude Cowork Claude Chat

Source

https://github.com/BilkentAudio/Wwise-MCP

By BilkentAudio

How to Get It

Option 1: Claude Desktop AppOpen the Customize panel in the sidebar → browse connectors → search and add. Works in Claude Code, Claude Cowork, and Claude Chat.

Option 2: Paste into Claude CodeCopy the command below and paste it into a Claude Code conversation. Claude will run it for you.

Command

claude mcp add Wwise-MCP -- npx -y Wwise-MCP

Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.

CostFree

Trust Signals Auto-scanned

Stars45Last updated2026-06-01LicenseApache-2.0 (OK for commercial use)Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-06-01 · scanner v1

Data & Access

Data processingPrompts sent to Anthropic API. Enterprise/Team plans exclude training.

Community Pulse Emerging

Discussed on Hacker News

Ask HN: Is there a differentiable measure on the equality of two sets? — Hacker News · 3 pts

1 mentions across 1 sources

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.

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 Scores

Evaluation

Ease of Use

3/5

Versatility

2/5

Reliability

3/5

Security

3/5

Overall score2.75 / 5.00 UsableEvaluatedJun 2026

Scored from trust signals (evidence-eval-v1): 45 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.

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