（點擊上方圖片觀看本課程的影片）
本課程著重於如何參與 MCP 社群、為 MCP 生態系統作出貢獻，以及遵循協作開發的最佳實踐。了解如何參與開源 MCP 項目對於希望塑造此技術未來的人至關重要。
完成本課程後，您將能夠：
- 了解 MCP 社群與生態系統的結構
- 有效參與 MCP 社群論壇與討論
- 為 MCP 開源倉庫作出貢獻
- 創建並分享自定義 MCP 工具與伺服器
- 遵循 MCP 開發與協作的最佳實踐
- 探索 MCP 開發的社群資源與框架
MCP 生態系統由多個組件與參與者組成，共同推進協議的發展。
- 核心協議維護者：官方 Model Context Protocol GitHub 組織 負責維護 MCP 規範與參考實現。
- 工具開發者：創建 MCP 工具與伺服器的個人或團隊。
- 整合提供者：將 MCP 整合至其產品與服務的公司。
- 終端使用者：在應用中使用 MCP 的開發者與組織。
- 貢獻者：為社群提供程式碼、文件或其他資源的成員。
- MCP 客戶端 - 支援 MCP 整合的客戶端列表。
- 社群 MCP 伺服器 - 不斷增長的社群開發 MCP 伺服器列表。
- Awesome MCP Servers - 精選 MCP 伺服器列表。
- PulseMCP - 發現 MCP 資源的社群中心與電子報。
- Discord 伺服器 - 與 MCP 開發者交流。
- 特定語言的 SDK 實現。
- 部落格文章與教學。
MCP 生態系統歡迎多種形式的貢獻：
-
程式碼貢獻：
- 核心協議增強
- 錯誤修復
- 工具與伺服器實現
- 不同語言的客戶端/伺服器庫
-
文件：
- 改善現有文件
- 創建教學與指南
- 翻譯文件
- 創建範例與示例應用
-
社群支持：
- 在論壇與討論中回答問題
- 測試與報告問題
- 組織社群活動
- 指導新貢獻者
要為核心 MCP 協議或官方實現作出貢獻，請遵循 官方貢獻指南 中的原則：
- 簡單與極簡主義：MCP 規範對新增概念設有高標準。新增規範比移除規範更容易。
- 具體方法：規範更改應基於具體的實現挑戰，而非推測性想法。
- 提案階段：
- 定義：探索問題範疇，驗證其他 MCP 使用者是否面臨類似問題。
- 原型：構建示例解決方案並展示其實際應用。
- 撰寫：基於原型撰寫規範提案。
# Fork the repository
git clone https://github.com/YOUR-USERNAME/modelcontextprotocol.git
cd modelcontextprotocol
# Install dependencies
npm install
# For schema changes, validate and generate schema.json:
npm run check:schema:ts
npm run generate:schema
# For documentation changes
npm run check:docs
npm run format
# Preview documentation locally (optional):
npm run serve:docs// Original code with bug in the typescript-sdk
export function validateResource(resource: unknown): resource is MCPResource {
if (!resource || typeof resource !== 'object') {
return false;
}
// Bug: Missing property validation
// Current implementation:
const hasName = 'name' in resource;
const hasSchema = 'schema' in resource;
return hasName && hasSchema;
}
// Fixed implementation in a contribution
export function validateResource(resource: unknown): resource is MCPResource {
if (!resource || typeof resource !== 'object') {
return false;
}
// Improved validation
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;
}# Example contribution: A CSV data processing tool for the MCP standard library
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:
# Extract parameters
operation = request.parameters.get("operation")
output_format = request.parameters.get("outputFormat", "json")
# Get CSV data from either direct data or URL
df = await self._get_dataframe(request)
# Process based on requested operation
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"""
# Implementation would include various transformations
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}")成功為 MCP 項目作出貢獻的秘訣：
- 從小開始：從文件、錯誤修復或小型增強開始。
- 遵循風格指南：遵守項目的程式碼風格與慣例。
- 撰寫測試：為程式碼貢獻包含單元測試。
- 記錄您的工作：為新功能或更改添加清晰的文件。
- 提交目標明確的 PR：保持拉取請求專注於單一問題或功能。
- 回應反饋：積極回應對貢獻的反饋。
# Clone the repository
git clone https://github.com/modelcontextprotocol/typescript-sdk.git
cd typescript-sdk
# Create a new branch for your contribution
git checkout -b feature/my-contribution
# Make your changes
# ...
# Run tests to ensure your changes don't break existing functionality
npm test
# Commit your changes with a descriptive message
git commit -am "Fix validation in resource handler"
# Push your branch to your fork
git push origin feature/my-contribution
# Create a pull request from your branch to the main repository
# Then engage with feedback and iterate on your PR as needed創建並分享自定義 MCP 伺服器是為 MCP 生態系統作出貢獻的最有價值方式之一。社群已經開發了數百個針對各種服務與使用案例的伺服器。
以下框架可簡化 MCP 伺服器開發：
-
官方 SDK：
-
社群框架：
- MCP-Framework - 使用 TypeScript 優雅快速地構建 MCP 伺服器。
- MCP Declarative Java SDK - 使用 Java 註解驅動的 MCP 伺服器。
- Quarkus MCP Server SDK - Java MCP 伺服器框架。
- Next.js MCP Server Template - MCP 伺服器的 Next.js 起始項目。
// 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// pom.xml configuration for a shareable MCP tool package
<!--
<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<>();
// Schema definition...
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;
// Call weather API
Map<String, Object> forecast = getForecast(location, days);
// Build response
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) {
// Implementation would call weather API
// Simplified example
Map<String, Object> result = new HashMap<>();
// Add forecast data...
return result;
}
}
// Build and publish using Maven
// mvn clean package
// mvn deploy# Directory structure for a PyPI package:
# mcp_nlp_tools/
# ├── LICENSE
# ├── README.md
# ├── setup.py
# ├── mcp_nlp_tools/
# │ ├── __init__.py
# │ ├── sentiment_tool.py
# │ └── translation_tool.py
# Example setup.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",
)
"""
# Example NLP tool implementation (sentiment_tool.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"):
# Load the sentiment analysis model
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:
# Extract parameters
text = request.parameters.get("text")
include_score = request.parameters.get("includeScore", True)
# Analyze sentiment
sentiment_result = self.sentiment_analyzer(text)[0]
# Format result
result = {
"sentiment": sentiment_result["label"],
"text": text
}
if include_score:
result["score"] = sentiment_result["score"]
# Return result
return ToolResponse(result=result)
except Exception as e:
raise ToolExecutionException(f"Sentiment analysis failed: {str(e)}")
# To publish:
# python setup.py sdist bdist_wheel
# python -m twine upload dist/*與社群分享 MCP 工具時：
-
完整文件：
- 記錄目的、使用方法與範例。
- 解釋參數與返回值。
- 記錄任何外部依賴。
-
錯誤處理：
- 實現穩健的錯誤處理。
- 提供有用的錯誤訊息。
- 優雅地處理邊界情況。
-
性能考量：
- 優化速度與資源使用。
- 適當時實現緩存。
- 考慮可擴展性。
-
安全性：
- 使用安全的 API 密鑰與身份驗證。
- 驗證與清理輸入。
- 為外部 API 調用實施速率限制。
-
測試：
- 包括全面的測試覆蓋。
- 使用不同的輸入類型與邊界情況進行測試。
- 記錄測試程序。
有效的協作是 MCP 生態系統蓬勃發展的關鍵。
- GitHub 問題與討論
- Microsoft Tech Community
- Discord 與 Slack 頻道
- Stack Overflow（標籤：
model-context-protocol或mcp）
審查 MCP 貢獻時：
- 清晰度：程式碼是否清晰且有良好文件？
- 正確性：是否按預期運作？
- 一致性：是否遵循項目慣例？
- 完整性：是否包含測試與文件？
- 安全性：是否存在安全問題？
開發 MCP 時：
- 協議版本控制：遵守工具支援的 MCP 協議版本。
- 客戶端兼容性：考慮向後兼容性。
- 伺服器兼容性：遵循伺服器實現指南。
- 重大更改：清楚記錄任何重大更改。
一個重要的社群貢獻可以是開發一個公共 MCP 工具註冊表。
# Example schema for a community tool registry API
from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel, Field, HttpUrl
from typing import List, Optional
import datetime
import uuid
# Models for the tool registry
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)
# FastAPI application for the registry
app = FastAPI(title="MCP Tool Registry")
# In-memory database for this example
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"}- MCP 社群多元化，歡迎各種形式的貢獻。
- 為 MCP 作出貢獻可以從核心協議增強到自定義工具。
- 遵循貢獻指南可提高 PR 被接受的機率。
- 創建並分享 MCP 工具是增強生態系統的寶貴方式。
- 社群協作對 MCP 的成長與改進至關重要。
- 根據您的技能與興趣，識別 MCP 生態系統中您可以作出貢獻的領域。
- Fork MCP 倉庫並設置本地開發環境。
- 創建一個小型增強、錯誤修復或工具，造福社群。
- 使用適當的測試與文件記錄您的貢獻。
- 向相關倉庫提交拉取請求。
下一步：早期採用的教訓
免責聲明：
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