navidrome-mcp-connector

name	navidrome-mcp-connector
description	MCP connector for Navidrome music server – enables AI-powered music library management, playlist curation, and streaming configuration through Claude
license	GPL
tags	["creative","music","mcp","navidrome","streaming"]
difficulty	beginner
time_to_master	4-8 weeks
version	1.0.0

Navidrome MCP Connector

Overview

This skill enables Claude to interact with Navidrome through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Navidrome's Subsonic API, allowing natural language control of Navidrome operations, intelligent automation, and AI-powered assistance for Navidrome workflows.

When to Use This Skill

• Playlist generation from mood/genre descriptions
• Library scanning and metadata management
• User and sharing configuration
• Transcoding settings optimization
• Internet radio station management

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Navidrome        │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (Subsonic API  )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Navidrome operations as tools Claude can invoke. The server translates natural language intentions into Subsonic API calls.

Key Endpoints/Interfaces

getArtists, getPlaylists, createPlaylist, search3, getAlbumList2

Implementation

// Navidrome MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "navidrome-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Navidrome resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Navidrome Subsonic API
    const response = await fetch(`${BASE_URL}getArtists`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Navidrome",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}getArtists`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Navidrome data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}getArtists`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "navidrome-mcp-connector": {
      "command": "node",
      "args": ["path/to/navidrome-mcp-connector/index.js"],
      "env": {
        "NAVIDROME_API_KEY": "your-api-key",
        "NAVIDROME_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Navidrome API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Navidrome API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Create a playlist of all jazz albums from the 1960s and share it with the family account"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Navidrome Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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