MCP QQ Music Test Server
Uses the qqmusic-api-python library from GitHub to provide music search functionality for QQ Music, allowing users to search for music tracks with keywords and retrieve song information including IDs, titles, and release dates.
Click on "Install 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 QQ Music Test Serversearch for Jay Chou's latest songs"
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 QQ音乐测试服务器
这是一个通过MCP(模块化控制协议)提供QQ音乐搜索功能的测试服务器。该服务器允许您使用关键词搜索音乐曲目,并返回相关歌曲信息。
🌐 在线宣传页 — 可视化了解功能特性与工作流程
该demo仅用于测试和学习目的,其中音乐检索使用的是qqmusic-api-python。
功能特点
支持MCP使用关键词搜索音乐曲目
Related MCP server: qq-music-mcp
环境要求
已安装了uv
Python 3.13
安装说明
克隆此仓库
git clone https://github.com/Samge0/mcp-qqmusic-test-server.git安装依赖:
uv sync
使用方法
配置mcp:
{
"mcpServers": {
"mcp-qqmusic-test-server": {
"command": "uv",
"args": [
"--directory",
"{put your local dir here}/mcp-qqmusic-test-server",
"run",
"main.py"
]
}
}
}测试搜索音乐
函数: search_music
参数:
keyword(字符串,必需):搜索关键词或短语page(整数,可选):分页页码(默认值:1)num(整数,可选):返回结果的最大数量(默认值:20)
返回值:
返回包含以下属性的对象数组:
id:歌曲IDmid:音乐IDname:歌曲名称pmid:播放音乐IDsubtitle:歌曲副标题time_public:发布时间title:歌曲标题
示例响应:
[
{
"id": "123456",
"mid": "001Qu4I30eVFYb",
"name": "七里香",
"pmid": "",
"subtitle": "",
"time_public": "2004-08-03",
"title": "七里香"
}
]Available Tools
1 toolsearch_musicB
Search for music tracks
Args: keyword: Search keyword or phrase page: Page number for pagination (default: 1) num: Maximum number of results to return (default: 20)
Returns: List of music tracks matching the search criteria
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | ||
| page | No | ||
| num | No |
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. While it mentions pagination and result limits, it doesn't describe important behavioral aspects like rate limits, authentication requirements, error conditions, or what happens with invalid inputs. For a search tool with zero annotation coverage, this leaves significant gaps.
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 well-structured with clear sections for Args and Returns. Each sentence earns its place by providing necessary information. It could be slightly more front-loaded with the main purpose, but overall it's efficient and organized.
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 tool's moderate complexity (3 parameters, search functionality) and the absence of both annotations and output schema, the description provides basic but incomplete coverage. It explains what the tool does and documents parameters, but lacks important context about behavior, constraints, and return format details that would be needed for comprehensive understanding.
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 0%, so the description must compensate. It successfully adds meaning for all three parameters: explaining that 'keyword' is for search terms, 'page' handles pagination, and 'num' controls result count. It also provides default values. This goes well beyond what the bare schema provides, though it doesn't explain parameter constraints or formats.
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 'Search for music tracks' which is a specific verb+resource combination. It effectively communicates the tool's purpose. However, with no sibling tools mentioned, there's no opportunity to differentiate from alternatives, preventing a perfect score.
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 no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, constraints, or typical use cases. The only usage information is implicit from the parameter descriptions, which isn't sufficient for explicit guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'search_music' has a clear, distinct purpose that cannot be confused with any other tool in this server.
The naming pattern cannot be evaluated for consistency across multiple tools, but the single tool name 'search_music' follows a clear verb_noun convention that is appropriate and readable. There are no inconsistencies to assess.
A single tool is too few for a music server's apparent scope, which typically involves operations like playing, pausing, getting recommendations, managing playlists, or accessing user libraries. This minimal set severely limits functionality and suggests an incomplete implementation.
The server is severely incomplete for a music domain, offering only search functionality without any way to play, queue, control playback, access user data, or manage music collections. This creates significant gaps that will cause agent failures in typical music-related workflows.
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