Skylv Mcp Server Creator

Build and run Model Context Protocol (MCP) servers with customizable tools and resources using TypeScript and the MCP SDK.

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

1. 错误处理：始终返回有意义的错误信息
2. 类型安全：使用 Zod 严格验证输入
3. 日志记录：使用 console.error 记录关键事件
4. 性能：长时间操作使用流式响应
5. 安全：不记录敏感信息，定期清理日志

Usage

1. Install the skill
2. Configure as needed
3. Run with OpenClaw