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-Serve 多语言翻译服务 translate the text ##Welcome to our application## to Spanish"
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（Model Context Protocol）服务是一种用于扩展 AI 模型能力的协议实现，允许 AI 模型与外部服务进行交互，从而增强其功能。本项目是一个基于 MCP 协议的自定义服务，专门用于处理多语言翻译和资源管理。
Related MCP server: Translation MCP Server
本项目提供两个主要功能：
自动识别并提取文本中被 ## 包裹的内容（如 ##创建人##）
将提取的内容发送到指定的 API 进行多语言资源注册
支持自动生成资源别名（ResourceAlias）
从 API 下载最新的多语言资源文件
自动将资源文件复制到项目的语言目录中（src/lang/source/）
首先，克隆项目并安装依赖：
git clone https://github.com/oofengoo/mcp-serve-learn
cd mcp-serve
npm install然后构建项目：
npm run build将mcp-servers.json文件中的配置复制到 VSCode 设置中：
打开 VSCode 设置（文件 > 首选项 > 设置）
点击"在 settings.json 中编辑"
将以下内容添加到 settings.json 中：
{
"mcp": {
"servers": {
"mcp-server": {
"command": "node",
"args": [
"D:\\Users\\Administrator\\Documents\\mcp-serve\\dist\\index.js"
],
"env": {
"Token": "",
"URL": "",
"PORT": ""
}
}
}
}
}Token: API 认证令牌（Cookie 值）
URL: API 服务器地址
PORT: 服务端口
本项目使用 TypeScript 开发，主要文件结构：
src/index.ts: MCP 服务器实现，包含工具定义
src/api.ts: API 接口封装
src/request.ts: HTTP 请求工具
| Name | Required | Description | Default |
|---|---|---|---|
| rootPath | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
copyI18ntranslationI18nScored across 2 tools
The two tools have overlapping purposes with no descriptions to clarify differences. 'copyI18n' and 'translationI18n' both appear to handle i18n (internationalization) tasks, making them indistinguishable. An agent cannot determine when to use one versus the other, leading to high misselection risk.
The naming shows mixed conventions: 'copyI18n' uses camelCase while 'translationI18n' uses a similar style but with a different prefix. Both include 'I18n', providing some thematic consistency, but the lack of a clear verb_noun pattern and the variation in prefixes ('copy' vs. 'translation') reduces predictability.
With only 2 tools for a '多语言翻译服务' (multilingual translation service), the count feels too thin. A translation service typically requires more operations, such as translate_text, detect_language, or manage_translations. This minimal set limits functionality and suggests an incomplete implementation.
The tool surface is severely incomplete for a translation service. There are no tools for core operations like translating text, detecting languages, or managing translation keys. The two vague tools do not cover the domain's essential workflows, leaving significant gaps that will cause agent failures.
MCP server for Translation Services
8 AI services via MCP: content, code, image, docs, data extraction, translation, SEO, email.
MCP server enabling AI agents to manage Bitrix24 features via standardized protocol
OCR, transcription, file extraction, and image generation for AI agents via MCP.