AI 智链谷
问苏一二 ↗社区交流 ·
社区与贡献
开源教程转载｜社区与贡献。Microsoft 及项目贡献者编写，智链谷平台运营整理。
> 开源教程转载 · 由智链谷平台运营账号整理发布。作者：Microsoft 及项目贡献者。
> [查看原文](https://github.com/microsoft/mcp-for-beginners/blob/2f43408b764326df5fc928984c6dd7db2b8fb63e/translations/zh-CN/06-CommunityContributions/README.md) · MIT 许可，完整声明见文末。
# 社区与贡献
[](https://youtu.be/v1pvCYAWpRE)
_(点击上方图片观看本课视频)_
## 概述
本课重点介绍如何参与MCP社区、贡献于MCP生态系统，以及遵循协作开发的最佳实践。理解如何参与开源MCP项目对于希望塑造该技术未来的人至关重要。
## 学习目标
完成本课后，你将能够：
- 了解MCP社区和生态系统的结构
- 有效参与MCP社区论坛和讨论
- 贡献于MCP开源代码库
- 创建并共享自定义MCP工具和服务器
- 遵循MCP开发与协作的最佳实践
- 发现适用于MCP开发的社区资源和框架
## MCP社区生态系统
MCP生态系统由多个组件和参与者组成，共同推动协议的发展。
### 关键社区组成部分
1. <strong>核心协议维护者</strong>：官方[Model Context Protocol GitHub组织](https://github.com/modelcontextprotocol)维护核心MCP规范和参考实现
2. <strong>工具开发者</strong>：创建MCP工具和服务器的个人和团队
3. <strong>集成提供商</strong>：将MCP集成到其产品和服务中的公司
4. <strong>终端用户</strong>：在应用中使用MCP的开发者和组织
5. <strong>贡献者</strong>：为代码、文档或其他资源做出贡献的社区成员
### 社区资源
#### 官方渠道
- [MCP GitHub组织](https://github.com/modelcontextprotocol)
- [MCP文档](https://modelcontextprotocol.io/)
- [MCP规范](https://spec.modelcontextprotocol.io/specification/2025-11-25/)
- [GitHub讨论区](https://github.com/orgs/modelcontextprotocol/discussions)
- [MCP示例与服务器代码库](https://github.com/modelcontextprotocol/servers)
#### 社区驱动资源
- [MCP客户端](https://modelcontextprotocol.io/clients) - 支持MCP集成的客户端列表
- [社区MCP服务器](https://github.com/modelcontextprotocol/servers?tab=readme-ov-file#-community-servers) - 不断扩展的社区开发MCP服务器列表
- [Awesome MCP Servers](https://github.com/wong2/awesome-mcp-servers) - 精选MCP服务器列表
- [PulseMCP](https://www.pulsemcp.com/) - 社区中心与通讯，用于发现MCP资源
- [Remote OpenClaw](https://www.remoteopenclaw.com/) - 免费的MCP服务器、代理技能和插件可搜索目录
- [Discord服务器](https://discord.gg/jHEGxQu2a5) - 与MCP开发者连接
- 语言特定的SDK实现
- 博客文章与教程
## 贡献于MCP
### 贡献类型
MCP生态系统欢迎各种类型的贡献：
1. <strong>代码贡献</strong>：
- 核心协议增强
- 修复错误
- 工具和服务器实现
- 不同语言的客户端/服务器库
2. <strong>文档</strong>：
- 改进现有文档
- 创建教程和指南
- 翻译文档
- 创建示例和样例应用
3. <strong>社区支持</strong>：
- 回答论坛和讨论中的问题
- 测试和报告问题
- 组织社区活动
- 指导新贡献者
### 贡献流程：核心协议
贡献于核心MCP协议或官方实现，请遵循[官方贡献指南](https://github.com/modelcontextprotocol/modelcontextprotocol/blob/main/CONTRIBUTING.md)中的原则：
1. <strong>简洁与极简主义</strong>：MCP规范对添加新概念设置了高标准。添加内容比删除更加容易。
2. <strong>具体方法</strong>：规范变更应基于具体实现中的挑战，而非推测性想法。
3. <strong>提案阶段</strong>：
- 定义：探索问题领域，验证其他MCP用户是否遇到类似问题
- 原型：构建示例解决方案并展示其实用性
- 编写：基于原型撰写规范提案
### 开发环境搭建
```bash
# 分叉仓库
git clone https://github.com/YOUR-USERNAME/modelcontextprotocol.git
cd modelcontextprotocol
# 安装依赖
npm install
# 对于模式更改，验证并生成 schema.json：
npm run check:schema:ts
npm run generate:schema
# 对于文档更改
npm run check:docs
npm run format
# 本地预览文档（可选）：
npm run serve:docs
```
### 示例：贡献一个错误修复
```javascript
// typescript-sdk 中存在错误的原始代码
export function validateResource(resource: unknown): resource is MCPResource {
if (!resource || typeof resource !== 'object') {
return false;
}
// 错误：缺少属性验证
// 当前实现：
const hasName = 'name' in resource;
const hasSchema = 'schema' in resource;
return hasName && hasSchema;
}
// 贡献中的修复实现
export function validateResource(resource: unknown): resource is MCPResource {
if (!resource || typeof resource !== 'object') {
return false;
}
// 改进的验证
const hasName = 'name' in resource && typeof (resource as MCPResource).name === 'string';
const hasSchema = 'schema' in resource && typeof (resource as MCPResource).schema === 'object';
const hasDescription = !('description' in resource) || typeof (resource as MCPResource).description === 'string';
return hasName && hasSchema && hasDescription;
}
```
### 示例：贡献一个标准库新工具
```python
# 示例贡献：用于MCP标准库的CSV数据处理工具
from mcp_tools import Tool, ToolRequest, ToolResponse, ToolExecutionException
import pandas as pd
import io
import json
from typing import Dict, Any, List, Optional
class CsvProcessingTool(Tool):
"""
Tool for processing and analyzing CSV data.
This tool allows models to extract information from CSV files,
run basic analysis, and convert data between formats.
"""
def get_name(self):
return "csvProcessor"
def get_description(self):
return "Processes and analyzes CSV data"
def get_schema(self):
return {
"type": "object",
"properties": {
"csvData": {
"type": "string",
"description": "CSV data as a string"
},
"csvUrl": {
"type": "string",
"description": "URL to a CSV file (alternative to csvData)"
},
"operation": {
"type": "string",
"enum": ["summary", "filter", "transform", "convert"],
"description": "Operation to perform on the CSV data"
},
"filterColumn": {
"type": "string",
"description": "Column to filter by (for filter operation)"
},
"filterValue": {
"type": "string",
"description": "Value to filter for (for filter operation)"
},
"outputFormat": {
"type": "string",
"enum": ["json", "csv", "markdown"],
"default": "json",
"description": "Output format for the processed data"
}
},
"oneOf": [
{"required": ["csvData", "operation"]},
{"required": ["csvUrl", "operation"]}
]
}
async def execute_async(self, request: ToolRequest) -> ToolResponse:
try:
# 提取参数
operation = request.parameters.get("operation")
output_format = request.parameters.get("outputFormat", "json")
# 从直接数据或URL获取CSV数据
df = await self._get_dataframe(request)
# 根据请求的操作进行处理
result = {}
if operation == "summary":
result = self._generate_summary(df)
elif operation == "filter":
column = request.parameters.get("filterColumn")
value = request.parameters.get("filterValue")
if not column:
raise ToolExecutionException("filterColumn is required for filter operation")
result = self._filter_data(df, column, value)
elif operation == "transform":
result = self._transform_data(df, request.parameters)
elif operation == "convert":
result = self._convert_format(df, output_format)
else:
raise ToolExecutionException(f"Unknown operation: {operation}")
return ToolResponse(result=result)
except Exception as e:
raise ToolExecutionException(f"CSV processing failed: {str(e)}")
async def _get_dataframe(self, request: ToolRequest) -> pd.DataFrame:
"""Gets a pandas DataFrame from either CSV data or URL"""
if "csvData" in request.parameters:
csv_data = request.parameters.get("csvData")
return pd.read_csv(io.StringIO(csv_data))
elif "csvUrl" in request.parameters:
csv_url = request.parameters.get("csvUrl")
return pd.read_csv(csv_url)
else:
raise ToolExecutionException("Either csvData or csvUrl must be provided")
def _generate_summary(self, df: pd.DataFrame) -> Dict[str, Any]:
"""Generates a summary of the CSV data"""
return {
"columns": df.columns.tolist(),
"rowCount": len(df),
"columnCount": len(df.columns),
"numericColumns": df.select_dtypes(include=['number']).columns.tolist(),
"categoricalColumns": df.select_dtypes(include=['object']).columns.tolist(),
"sampleRows": json.loads(df.head(5).to_json(orient="records")),
"statistics": json.loads(df.describe().to_json())
}
def _filter_data(self, df: pd.DataFrame, column: str, value: str) -> Dict[str, Any]:
"""Filters the DataFrame by a column value"""
if column not in df.columns:
raise ToolExecutionException(f"Column '{column}' not found")
filtered_df = df[df[column].astype(str).str.contains(value)]
return {
"originalRowCount": len(df),
"filteredRowCount": len(filtered_df),
"data": json.loads(filtered_df.to_json(orient="records"))
}
def _transform_data(self, df: pd.DataFrame, params: Dict[str, Any]) -> Dict[str, Any]:
"""Transforms the data based on parameters"""
# 实现将包括各种转换
return {
"status": "success",
"message": "Transformation applied"
}
def _convert_format(self, df: pd.DataFrame, format: str) -> Dict[str, Any]:
"""Converts the DataFrame to different formats"""
if format == "json":
return {
"data": json.loads(df.to_json(orient="records")),
"format": "json"
}
elif format == "csv":
return {
"data": df.to_csv(index=False),
"format": "csv"
}
elif format == "markdown":
return {
"data": df.to_markdown(),
"format": "markdown"
}
else:
raise ToolExecutionException(f"Unsupported output format: {format}")
```
### 贡献指南
成功贡献于MCP项目：
1. <strong>从小处做起</strong>：从文档、错误修复或小改进开始
2. <strong>遵循风格指南</strong>：遵守项目代码风格和约定
3. <strong>编写测试</strong>：为代码贡献包含单元测试
4. <strong>记录工作</strong>：为新功能或改动添加清晰文档
5. **提交针对性PR**：保持PR聚焦于单一问题或功能
6. <strong>积极反馈</strong>：对你的贡献反馈做出响应
### 示例贡献工作流程
```bash
# 克隆仓库
git clone https://github.com/modelcontextprotocol/typescript-sdk.git
cd typescript-sdk
# 为你的贡献创建一个新分支
git checkout -b feature/my-contribution
# 进行你的修改
# ...
# 运行测试以确保你的修改不会破坏现有功能
npm test
# 使用描述性信息提交你的修改
git commit -am "Fix validation in resource handler"
# 将你的分支推送到你的分叉仓库
git push origin feature/my-contribution
# 从你的分支向主仓库创建拉取请求
# 然后根据反馈参与讨论并按需迭代你的拉取请求
```
## 创建与共享MCP服务器
为MCP生态系统贡献最有价值的方式之一是创建并共享自定义MCP服务器。社区已经开发了数百个针对不同服务和用例的服务器。
### MCP服务器开发框架
有多个框架可简化MCP服务器开发：
1. **官方SDK**（符合[MCP规范2025-11-25](https://spec.modelcontextprotocol.io/specification/2025-11-25/)）：
- [TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk)
- [Python SDK](https://github.com/modelcontextprotocol/python-sdk)
- [C# SDK](https://github.com/modelcontextprotocol/csharp-sdk)
- [Go SDK](https://github.com/modelcontextprotocol/go-sdk)
- [Java SDK](https://github.com/modelcontextprotocol/java-sdk)
- [Kotlin SDK](https://github.com/modelcontextprotocol/kotlin-sdk)
- [Swift SDK](https://github.com/modelcontextprotocol/swift-sdk)
- [Rust SDK](https://github.com/modelcontextprotocol/rust-sdk)
2. <strong>社区框架</strong>：
- [MCP-Framework](https://mcp-framework.com/) - 使用TypeScript优雅快速构建MCP服务器
- [MCP声明式Java SDK](https://github.com/codeboyzhou/mcp-declarative-java-sdk) - 基于注解的Java MCP服务器
- [Quarkus MCP服务器SDK](https://github.com/quarkiverse/quarkus-mcp-server) - MCP服务器Java框架
- [Next.js MCP服务器模板](https://github.com/vercel-labs/mcp-for-next.js) - MCP服务器Next.js入门项目
### 开发可共享工具
#### .NET示例：创建共享工具包
```csharp
// Create a new .NET library project
// dotnet new classlib -n McpFinanceTools
using Microsoft.Mcp.Tools;
using System.Threading.Tasks;
using System.Net.Http;
using System.Text.Json;
namespace McpFinanceTools
{
// Stock quote tool
public class StockQuoteTool : IMcpTool
{
private readonly HttpClient _httpClient;
public StockQuoteTool(HttpClient httpClient = null)
{
_httpClient = httpClient ?? new HttpClient();
}
public string Name => "stockQuote";
public string Description => "Gets current stock quotes for specified symbols";
public object GetSchema()
{
return new {
type = "object",
properties = new {
symbol = new {
type = "string",
description = "Stock symbol (e.g., MSFT, AAPL)"
},
includeHistory = new {
type = "boolean",
description = "Whether to include historical data",
default = false
}
},
required = new[] { "symbol" }
};
}
public async Task<ToolResponse> ExecuteAsync(ToolRequest request)
{
// Extract parameters
string symbol = request.Parameters.GetProperty("symbol").GetString();
bool includeHistory = false;
if (request.Parameters.TryGetProperty("includeHistory", out var historyProp))
{
includeHistory = historyProp.GetBoolean();
}
// Call external API (example)
var quoteResult = await GetStockQuoteAsync(symbol);
// Add historical data if requested
if (includeHistory)
{
var historyData = await GetStockHistoryAsync(symbol);
quoteResult.Add("history", historyData);
}
// Return formatted result
return new ToolResponse {
Result = JsonSerializer.SerializeToElement(quoteResult)
};
}
private async Task<Dictionary<string, object>> GetStockQuoteAsync(string symbol)
{
// Implementation would call a real stock API
// This is a simplified example
return new Dictionary<string, object>
{
["symbol"] = symbol,
["price"] = 123.45,
["change"] = 2.5,
["percentChange"] = 1.2,
["lastUpdated"] = DateTime.UtcNow
};
}
private async Task<object> GetStockHistoryAsync(string symbol)
{
// Implementation would get historical data
// Simplified example
return new[]
{
new { date = DateTime.Now.AddDays(-7).Date, price = 120.25 },
new { date = DateTime.Now.AddDays(-6).Date, price = 122.50 },
new { date = DateTime.Now.AddDays(-5).Date, price = 121.75 }
// More historical data...
};
}
}
}
// Create package and publish to NuGet
// dotnet pack -c Release
// dotnet nuget push bin/Release/McpFinanceTools.1.0.0.nupkg -s https://api.nuget.org/v3/index.json -k YOUR_API_KEY
```
#### Java示例：为工具创建Maven包
```java
// 用于可共享MCP工具包的pom.xml配置
<!--
<project>
<groupId>com.example</groupId>
<artifactId>mcp-weather-tools</artifactId>
<version>1.0.0</version>
<dependencies>
<dependency>
<groupId>com.mcp</groupId>
<artifactId>mcp-server</artifactId>
<version>1.0.0</version>
</dependency>
</dependencies>
<distributionManagement>
<repository>
<id>github</id>
<name>GitHub Packages</name>
<url>https://maven.pkg.github.com/username/mcp-weather-tools</url>
</repository>
</distributionManagement>
</project>
-->
package com.example.mcp.weather;
import com.mcp.tools.Tool;
import com.mcp.tools.ToolRequest;
import com.mcp.tools.ToolResponse;
import com.mcp.tools.ToolExecutionException;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.net.URI;
import java.util.HashMap;
import java.util.Map;
public class WeatherForecastTool implements Tool {
private final HttpClient httpClient;
private final String apiKey;
public WeatherForecastTool(String apiKey) {
this.httpClient = HttpClient.newHttpClient();
this.apiKey = apiKey;
}
@Override
public String getName() {
return "weatherForecast";
}
@Override
public String getDescription() {
return "Gets weather forecast for a specified location";
}
@Override
public Object getSchema() {
Map<String, Object> schema = new HashMap<>();
// 架构定义...
return schema;
}
@Override
public ToolResponse execute(ToolRequest request) {
try {
String location = request.getParameters().get("location").asText();
int days = request.getParameters().has("days") ?
request.getParameters().get("days").asInt() : 3;
// 调用天气API
Map<String, Object> forecast = getForecast(location, days);
// 构建响应
return new ToolResponse.Builder()
.setResult(forecast)
.build();
} catch (Exception ex) {
throw new ToolExecutionException("Weather forecast failed: " + ex.getMessage(), ex);
}
}
private Map<String, Object> getForecast(String location, int days) {
// 实现将调用天气API
// 简化示例
Map<String, Object> result = new HashMap<>();
// 添加预测数据...
return result;
}
}
// 使用Maven构建和发布
// mvn clean package
// mvn deploy
```
#### Python示例：发布PyPI包
```python
# PyPI 包的目录结构：
# mcp_nlp_tools/
# ├── 许可证
# ├── 自述文件.md
# ├── 安装脚本.py
# ├── mcp_nlp_tools/
# │ ├── 初始化文件.py
# │ ├── 情感工具.py
# │ └── 翻译工具.py
# 示例安装脚本.py
"""
from setuptools import setup, find_packages
setup(
name="mcp_nlp_tools",
version="0.1.0",
packages=find_packages(),
install_requires=[
"mcp_server>=1.0.0",
"transformers>=4.0.0",
"torch>=1.8.0"
],
author="Your Name",
author_email="your.email@example.com",
description="MCP tools for natural language processing tasks",
long_description=open("README.md").read(),
long_description_content_type="text/markdown",
url="https://github.com/username/mcp_nlp_tools",
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires=">=3.8",
)
"""
# 示例NLP工具实现（情感工具.py）
from mcp_tools import Tool, ToolRequest, ToolResponse, ToolExecutionException
from transformers import pipeline
import torch
class SentimentAnalysisTool(Tool):
"""MCP tool for sentiment analysis of text"""
def __init__(self, model_name="distilbert-base-uncased-finetuned-sst-2-english"):
# 加载情感分析模型
self.sentiment_analyzer = pipeline("sentiment-analysis", model=model_name)
def get_name(self):
return "sentimentAnalysis"
def get_description(self):
return "Analyzes the sentiment of text, classifying it as positive or negative"
def get_schema(self):
return {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to analyze for sentiment"
},
"includeScore": {
"type": "boolean",
"description": "Whether to include confidence scores",
"default": True
}
},
"required": ["text"]
}
async def execute_async(self, request: ToolRequest) -> ToolResponse:
try:
# 提取参数
text = request.parameters.get("text")
include_score = request.parameters.get("includeScore", True)
# 分析情感
sentiment_result = self.sentiment_analyzer(text)[0]
# 格式化结果
result = {
"sentiment": sentiment_result["label"],
"text": text
}
if include_score:
result["score"] = sentiment_result["score"]
# 返回结果
return ToolResponse(result=result)
except Exception as e:
raise ToolExecutionException(f"Sentiment analysis failed: {str(e)}")
# 发布步骤：
# python setup.py sdist bdist_wheel
# python -m twine upload dist/*
```
### 分享最佳实践
与社区共享MCP工具时：
1. <strong>完整文档</strong>：
- 记录目的、用法和示例
- 说明参数和返回值
- 记录任何外部依赖
2. <strong>错误处理</strong>：
- 实现健壮的错误处理
- 提供有用的错误信息
- 优雅处理边缘情况
3. <strong>性能考量</strong>：
- 优化速度和资源使用
- 适时实现缓存
- 考虑可扩展性
4. <strong>安全性</strong>：
- 使用安全的API密钥和认证
- 验证并清理输入
- 对外部API调用实施限流
5. <strong>测试</strong>：
- 包含全面测试覆盖
- 测试不同输入类型和边缘情况
- 记录测试流程
## 社区协作与最佳实践
有效的协作是MCP生态系统繁荣的关键。
### 交流渠道
- GitHub Issues和讨论区
- Microsoft技术社区
- Discord和Slack频道
- Stack Overflow（标签：`model-context-protocol`或`mcp`）
### 代码审查
审查MCP贡献时：
1. <strong>清晰度</strong>：代码是否清晰且文档完整？
2. <strong>正确性</strong>：代码是否按预期工作？
3. <strong>一致性</strong>：是否遵循项目约定？
4. <strong>完整性</strong>：是否包含测试和文档？
5. <strong>安全性</strong>：是否有安全隐患？
### 版本兼容性
开发MCP时：
1. <strong>协议版本管理</strong>：遵循你的工具支持的MCP协议版本
2. <strong>客户端兼容性</strong>：考虑向下兼容
3. <strong>服务器兼容性</strong>：遵循服务器实现规范
4. <strong>破坏性更改</strong>：明确记录任何不兼容更改
## 社区项目示例：MCP工具注册表
一个重要的社区贡献可以是开发一个MCP工具的公共注册表。
```python
# 社区工具注册API的示例架构
from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel, Field, HttpUrl
from typing import List, Optional
import datetime
import uuid
# 工具注册的模型
class ToolSchema(BaseModel):
"""JSON Schema for a tool"""
type: str
properties: dict
required: List[str] = []
class ToolRegistration(BaseModel):
"""Information for registering a tool"""
name: str = Field(..., description="Unique name for the tool")
description: str = Field(..., description="Description of what the tool does")
version: str = Field(..., description="Semantic version of the tool")
schema: ToolSchema = Field(..., description="JSON Schema for tool parameters")
author: str = Field(..., description="Author of the tool")
repository: Optional[HttpUrl] = Field(None, description="Repository URL")
documentation: Optional[HttpUrl] = Field(None, description="Documentation URL")
package: Optional[HttpUrl] = Field(None, description="Package URL")
tags: List[str] = Field(default_factory=list, description="Tags for categorization")
examples: List[dict] = Field(default_factory=list, description="Example usage")
class Tool(ToolRegistration):
"""Tool with registry metadata"""
id: uuid.UUID = Field(default_factory=uuid.uuid4)
created_at: datetime.datetime = Field(default_factory=datetime.datetime.now)
updated_at: datetime.datetime = Field(default_factory=datetime.datetime.now)
downloads: int = Field(default=0)
rating: float = Field(default=0.0)
ratings_count: int = Field(default=0)
# 用于注册的FastAPI应用
app = FastAPI(title="MCP Tool Registry")
# 此示例的内存数据库
tools_db = {}
@app.post("/tools", response_model=Tool)
async def register_tool(tool: ToolRegistration):
"""Register a new tool in the registry"""
if tool.name in tools_db:
raise HTTPException(status_code=400, detail=f"Tool '{tool.name}' already exists")
new_tool = Tool(**tool.dict())
tools_db[tool.name] = new_tool
return new_tool
@app.get("/tools", response_model=List[Tool])
async def list_tools(tag: Optional[str] = None):
"""List all registered tools, optionally filtered by tag"""
if tag:
return [tool for tool in tools_db.values() if tag in tool.tags]
return list(tools_db.values())
@app.get("/tools/{tool_name}", response_model=Tool)
async def get_tool(tool_name: str):
"""Get information about a specific tool"""
if tool_name not in tools_db:
raise HTTPException(status_code=404, detail=f"Tool '{tool_name}' not found")
return tools_db[tool_name]
@app.delete("/tools/{tool_name}")
async def delete_tool(tool_name: str):
"""Delete a tool from the registry"""
if tool_name not in tools_db:
raise HTTPException(status_code=404, detail=f"Tool '{tool_name}' not found")
del tools_db[tool_name]
return {"message": f"Tool '{tool_name}' deleted"}
```
## 关键要点
- MCP社区多元化，欢迎各种类型的贡献
- 贡献范围涵盖核心协议增强到自定义工具开发
- 遵循贡献指南能提高PR接受率
- 创建与共享MCP工具是提升生态系统的宝贵方式
- 社区协作对于MCP的成长和进步至关重要
## 练习
1. 根据你的技能和兴趣，确定可以贡献的MCP生态系统领域
2. Fork MCP代码库并搭建本地开发环境
3. 创建一个小改进、错误修复或有益社区的工具
4. 用适当的测试和文档记录你的贡献
5. 向合适的代码库提交pull request
## 附加资源
- [MCP社区项目](https://github.com/topics/model-context-protocol)
---
## 接下来
下一课：[早期采用中的经验教训](https://github.com/microsoft/mcp-for-beginners/blob/2f43408b764326df5fc928984c6dd7db2b8fb63e/translations/zh-CN/07-LessonsfromEarlyAdoption/README.md)
---
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## 版权与许可
```text
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
```