MCP MiniMax Music Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP MiniMax Music ServerGenerate a romantic acoustic guitar melody for a sunset scene"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP MiniMax Music Server
A Model Context Protocol (MCP) server implementation for AI-powered audio generation using the MiniMax Music API. Developed by Falah.G.Salieh.
📋 Table of Contents
Related MCP server: Mureka MCP Server
🎯 Overview
This MCP server enables AI agents to generate music and audio content using the MiniMax Music API through the Model Context Protocol. It provides seamless integration with MCP hosts like Claude Desktop, allowing AI agents to create music and audio based on text prompts.
✨ Features
AI-powered music generation
Support for MiniMax Music model
Two-step generation process with status checking
Seamless integration with Claude Desktop
Environment variable support for API keys
Detailed error handling and reporting
🔧 Prerequisites
Node.js (v16 or higher)
TypeScript (v5.3.3 or higher)
Claude Desktop (latest version)
AIML API Key
Windows/Linux/macOS operating system
📦 Installation
Clone the repository:
git clone https://github.com/yourusername/mcp-minimax-music-server.git cd mcp-minimax-music-serverInstall dependencies:
npm installBuild the server:
npm run build
⚙️ Configuration
Claude Desktop Configuration
Locate your Claude Desktop configuration file:
Windows:
%APPDATA%\Claude\claude_desktop_config.jsonLinux:
~/.config/Claude/claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.json
Add the following configuration:
{ "mcpServers": { "minimax-music-server": { "command": "node", "args": [ "G:\\mcp-minimax-music-server\\build\\index.js" ], "env": { "AIML_API_KEY": "Bearer your-aiml-api-key-here" } } } }⚠️ Important:
Replace the path with your actual server path
Add "Bearer " prefix to your API key
Use double backslashes in Windows paths
API Key Setup
Your AIML API key can be configured in two ways:
Environment Variables (Recommended)
Set in Claude Desktop config as shown above
Prefix with "Bearer " (include the space)
Example:
"AIML_API_KEY": "Bearer 3d90d64a000c4e6eb02df7e52d2166d2"
Direct Configuration
Pass the API key directly in the generation request
Less secure, but useful for testing
🚀 Usage
Basic Usage
The server provides a tool called generate_audio with the following parameters:
prompt(required): Text prompt for audio generationmodel(optional): Set to "minimax-music"reference_audio_url(optional): URL of reference audiogeneration_id(optional): ID from previous generation for status checking
Example Commands
Start new generation:
Generate audio with prompt "Create a romantic love song with gentle acoustic guitar and soft vocals"Check generation status:
Generate audio with generation_id "abc123" and prompt "Check status"
Response Format
{
"toolResult": {
"status": "completed",
"id": "generation-id",
"audio_file": {
"url": "https://cdn.example.com/audio.mp3",
"content_type": "audio/mpeg",
"file_name": "output.mp3",
"file_size": 1024000
}
}
}📚 API Reference
MiniMax Music Model
The server uses the MiniMax Music model which:
Specializes in music generation
Requires reference audio for style matching
Supports lyric generation with ## delimiters
Generates high-quality musical output
Generation Process
Step 1: Submit Generation
Send prompt and parameters
Receive generation ID
Step 2: Check Status
Poll status using generation ID
Download audio when complete
🔍 Troubleshooting
Common Issues
Authentication Errors (401)
Verify API key format (should start with "Bearer ")
Check API key validity
Ensure correct configuration in
claude_desktop_config.json
Path Issues
Use correct path format for your OS
Windows: Use double backslashes
Verify build directory exists
Generation Errors
Check prompt format (should be wrapped in ##...## for lyrics)
Ensure reference audio URL is accessible
Verify prompt length and content
Debug Steps
Rebuild the server:
npm run buildRestart Claude Desktop
Check server logs for errors
🤝 Contributing
Fork the repository
Create your feature branch
Commit your changes
Push to the branch
Create a Pull Request
📄 License
MIT License - see the LICENSE file for details
👤 Author
Falah.G.Salieh
Location: Baghdad, Iraq
Role: Developer & AI Integration Specialist
Year: 2025
🌟 Support
Need help? Here's how to get support:
Check the troubleshooting section
Open an issue in the repository
Contact the author directly
This project is part of the Model Context Protocol ecosystem, enabling seamless integration between AI agents and external services.
Available Tools
1 toolgenerate_audioB
Generate audio using AIML API. The process has two steps: 1) Submit generation request 2) Get the generated audio. If generation_id is not provided, it will start a new generation.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | The model to use for generation (stable-audio or minimax-music) | minimax-music |
| reference_audio_url | No | URL of the reference audio (required for minimax-music) | https://tand-dev.github.io/audio-hosting/spinning-head-271171.mp3 |
| prompt | Yes | The text prompt for audio generation. For minimax-music, wrap lyrics in ##...## | |
| api_key | No | Your AIML API Key (optional if set in environment variables) | |
| generation_id | No | Optional: The generation ID from a previous request to check status |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the two-step process and conditional behavior based on generation_id, but fails to cover critical aspects like authentication needs (beyond the optional api_key hint), rate limits, error handling, or what the output entails (e.g., audio file format, size). This is inadequate for a tool with potential mutations and external API calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose and key process steps. Both sentences earn their place by clarifying the tool's workflow, though it could be slightly more streamlined by integrating the conditional behavior more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an audio generation tool with no annotations and no output schema, the description is incomplete. It lacks details on what the tool returns (e.g., audio URL, generation status), error conditions, or performance expectations, leaving significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, only implying that generation_id relates to checking status of a previous request, which is already suggested in the schema's description. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate audio using AIML API' with a two-step process. It specifies the action (generate audio) and the resource (AIML API), but doesn't distinguish from siblings since there are none, making it clear but not fully optimized for differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance by explaining the two-step process and stating that if generation_id is not provided, it starts a new generation. However, it lacks explicit when-to-use scenarios, prerequisites, or alternatives, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.0- First observed
generate_audio
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_audio' has a clear, distinct purpose.
The single tool name follows a clear verb_noun pattern (generate_audio), and with only one tool, consistency is inherently perfect as there are no other names to compare against.
One tool is too few for a music server's apparent scope, which typically involves operations like listing, playing, or managing audio beyond just generation. This minimal set limits functionality and feels incomplete for the domain.
The tool surface is severely incomplete for a music server. It only covers audio generation, with no tools for retrieving, updating, deleting, or managing audio files, creating significant gaps that will cause agent failures in typical music-related tasks.
Maintenance
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