MCP Server Builder
功能说明
构建 Model Context Protocol 服务器，扩展 AI 能力边界。
MCP 协议概述
MCP 是 Anthropic 推出的 AI 模型上下文协议，让 AI 能调用外部工具和数据源。
项目结构
mcp-server/
├── package.json
├── tsconfig.json
├── src/
│ ├── index.ts # 主入口
│ ├── tools/ # 工具定义
│ └── resources/ # 资源定义
└── tsconfig.json
完整实现
1. 初始化项目
npm init -y
npm install @modelcontextprotocol/sdk zod
npm install -D typescript @types/node ts-node
// package.json
{
"name": "my-mcp-server",
"version": "1.0.0",
"type": "module",
"scripts": {
"build": "tsc",
"start": "node dist/index.js",
"dev": "ts-node src/index.ts"
},
"dependencies": {
"@modelcontextprotocol/sdk": "^0.5.0",
"zod": "^3.22.0"
}
}
// tsconfig.json
{
"compilerOptions": {
"target": "ES2022",
"module": "NodeNext",
"moduleResolution": "NodeNext",
"outDir": "./dist",
"rootDir": "./src",
"strict": true,
"esModuleInterop": true
},
"include": ["src/**/*"]
}
2. 定义工具
// src/tools/search.ts
import { z } from 'zod';
export const searchTool = {
name: 'web_search',
description: '搜索互联网获取最新信息',
inputSchema: z.object({
query: z.string().describe('搜索关键词'),
limit: z.number().optional().default(5).describe('返回结果数量')
}),
async handler(args: { query: string; limit?: number }) {
// 实际实现
const results = await performSearch(args.query, args.limit || 5);
return {
content: results.map(r => ({
type: 'text' as const,
text: `标题: ${r.title}\n链接: ${r.url}\n摘要: ${r.snippet}`
}))
};
}
};
3. 定义资源
// src/resources/knowledge.ts
export const knowledgeResources = {
uriPrefix: 'knowledge://',
list: async () => [
{
uri: 'knowledge://docs/latest',
name: '最新文档',
description: '系统最新文档版本',
mimeType: 'text/markdown'
}
],
read: async (uri: string) => {
if (uri === 'knowledge://docs/latest') {
return {
contents: [{
uri,
mimeType: 'text/markdown',
text: '# 最新文档\n\n...'
}]
};
}
throw new Error('Resource not found');
}
};
4. 主入口
// src/index.ts
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import {
CallToolRequestSchema,
ListToolsRequestSchema,
ListResourcesRequestSchema,
ReadResourceRequestSchema
} from '@modelcontextprotocol/sdk/types.js';
import { searchTool } from './tools/search.js';
import { knowledgeResources } from './resources/knowledge.js';
class MyMCPServer {
private server: Server;
constructor() {
this.server = new Server(
{ name: 'my-mcp-server', version: '1.0.0' },
{ capabilities: { tools: {}, resources: {} } }
);
this.setupToolHandlers();
this.setupResourceHandlers();
}
private setupToolHandlers() {
this.server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [
{
name: searchTool.name,
description: searchTool.description,
inputSchema: searchTool.inputSchema
}
]
}));
this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
if (name === 'web_search') {
return await searchTool.handler(args as any);
}
throw new Error(`Unknown tool: ${name}`);
});
}
private setupResourceHandlers() {
this.server.setRequestHandler(ListResourcesRequestSchema, async () => ({
resources: await knowledgeResources.list()
}));
this.server.setRequestHandler(ReadResourceRequestSchema, async (request) => {
return await knowledgeResources.read(request.params.uri);
});
}
async start() {
const transport = new StdioServerTransport();
await this.server.connect(transport);
console.error('MCP Server started on stdio');
}
}
new MyMCPServer().start().catch(console.error);
5. 更多工具示例
// 文件操作工具
export const fileTools = {
name: 'file_operations',
description: '读取、写入、列出文件',
inputSchema: z.object({
operation: z.enum(['read', 'write', 'list', 'delete']),
path: z.string(),
content: z.string().optional()
}),
async handler(args: any) {
const fs = await import('fs/promises');
switch (args.operation) {
case 'read': {
const content = await fs.readFile(args.path, 'utf-8');
return { content: [{ type: 'text', text: content }] };
}
case 'write': {
await fs.writeFile(args.path, args.content || '');
return { content: [{ type: 'text', text: 'File written successfully' }] };
}
case 'list': {
const files = await fs.readdir(args.path);
return { content: [{ type: 'text', text: files.join('\n') }] };
}
default:
throw new Error(`Unknown operation: ${args.operation}`);
}
}
};
// 数据库查询工具
export const dbTool = {
name: 'database_query',
description: '执行数据库查询',
inputSchema: z.object({
sql: z.string().describe('SQL查询语句'),
params: z.array(z.any()).optional()
}),
async handler(args: any) {
// 使用 mysql2 或 pg
// const pool = new Pool({ connectionString: process.env.DATABASE_URL });
// const result = await pool.query(args.sql, args.params);
return {
content: [{ type: 'text', text: JSON.stringify({ rows: [], count: 0 }) }]
};
}
};
测试
# 编译
npm run build
# 手动测试
echo '{"jsonrpc":"2.0","method":"tools/list","id":1}' | npm run dev
# MCP Inspector
npx @modelcontextprotocol/inspector npm run dev
部署
Claude Desktop
// ~/.config/claude-desktop/claude_desktop_config.json
{
"mcpServers": {
"my-mcp-server": {
"command": "node",
"args": ["/path/to/mcp-server/dist/index.js"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
Cursor / VS Code
在扩展设置中添加 MCP 服务器路径。
最佳实践
- 错误处理：始终返回有意义的错误信息
- 类型安全：使用 Zod 严格验证输入
- 日志记录：使用
console.error 记录关键事件
- 性能：长时间操作使用流式响应
- 安全：不记录敏感信息，定期清理日志
Usage
- Install the skill
- Configure as needed
- Run with OpenClaw
