Prediction-Examples-in-the-Alink-Tutorial
# Alink教程中的预测例子 Prediction Examples in the Alink Tutorial
Alink教程中的两个预测例子：输入年份，预测GMV；输入一段评论，分析其情感色彩（褒义贬义/正向负向）。
Two prediction examples in the Alink tutorial: input a year to predict GMV; input a piece of feedback to analyze its sentiment (positive/negative).## 工具列表 Tool List
本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。 本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。
| 工具 Tool | 描述 Description |
|-------|--------------------|
| pred_gmv | Alink教程第1章5.4节的Pipeline预测。输入年份，预测GMV。 |
| calc | Alink教程第23章4节的情感预测。输入一段评论，分析其情感色彩（褒义贬义/正向负向）。 |
## 检查服务 ## Inspector
工具在线测试： [https://mcp.xiaobenyang.com/inspector/1804079084099594](https://mcp.xiaobenyang.com/inspector/1804079084099594)
Online Tool test [https://mcp.xiaobenyang.com/inspector/1804079084099594](https://mcp.xiaobenyang.com/inspector/1804079084099594)
## 服务配置 MCP Server Config
> #### 如何获取 XBY-APIKEY ？ How to get XBY-APIKEY ?
> 访问小笨羊科技网站 [https://xiaobenyang.com](https://xiaobenyang.com)，注册用户即可获得APIKEY
> Visit XiaoBenYang website [https://xiaobenyang.com](https://xiaobenyang.com), register and get the APIKEY.
### SSE
```json
{
"mcpServers": {
"Alink教程中的预测例子": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "sse",
"url": "https://mcp.xiaobenyang.com/1804079084099594/sse"
}
}
}
```
### STREAMABLE HTTP
```json
{
"mcpServers": {
"Alink教程中的预测例子": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "streamable_http",
"url": "https://mcp.xiaobenyang.com/1804079084099594/mcp"
}
}
}
```
### STDIO
```json
{
"mcpServers": {
"Alink教程中的预测例子": {
"command": "npx",
"args": [
"-y",
"xiaobenyang-mcp"
],
"env": {
"XBY_APIKEY": "<YOUR_XBY_APIKEY>",
"mcpId": "1804079084099594",
},
"transport": "stdio"
}
}
}
```
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: one analyzes sentiment of text reviews, while the other predicts GMV based on year input. There is no overlap in functionality or potential for confusion between these unrelated domains.
The naming is inconsistent with 'calc' using a generic abbreviation and 'pred_gmv' using a more descriptive verb_noun pattern. While both are short, they follow different conventions without a clear unified naming strategy.
With only two tools, this server feels extremely thin for a tutorial prediction examples server. The tools cover completely unrelated domains (sentiment analysis and GMV prediction), suggesting either an incomplete implementation or a poorly scoped server that should likely be split into separate specialized servers.
For a tutorial examples server, there are significant gaps - each tool represents a single isolated example without supporting operations. There's no way to train models, evaluate predictions, or handle related tasks. The server feels like a collection of disconnected demonstration snippets rather than a coherent tool surface.