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mcp-apple-music

mcp-apple-music

by Cifero74
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_recommendations

Generate personalized Apple Music recommendations based on your listening preferences. Specify the number of recommendation groups to receive tailored music suggestions.

Instructions

Get personalised Apple Music recommendations.

Args: limit: Number of recommendation groups to return, 1–10 (default 5).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_recommendations"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full disclosure burden. It adds 'personalised' (indicating user-specific/history-based behavior) and 'recommendation groups' (indicating the return structure). However, it omits authentication requirements, rate limits, or caching behavior for the Apple Music API.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely efficient at two sentences. The first states purpose; the second documents the parameter. Every element earns its place, though the 'Args:' formatting is slightly informal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Appropriate for the tool's low complexity (one optional parameter) and presence of output schema. The description successfully bridges the schema coverage gap. Could be improved by noting the personalization/auth prerequisite, but sufficient for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Excellent compensation for 0% schema coverage. The Args section explicitly documents 'limit' as 'Number of recommendation groups to return', providing semantic meaning (groups vs tracks), valid range (1–10), and default value (5) that the schema completely lacks.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the specific verb 'Get' and resource 'personalised Apple Music recommendations', clarifying the tool fetches discovery content. However, it lacks explicit differentiation from siblings like 'search_catalog' or 'get_library_songs' to help the agent choose between discovery methods.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidelines provided for when to use this tool versus alternatives. The agent cannot determine whether to use recommendations, search, or library retrieval for music discovery without external reasoning.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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