模型上下文协议允许人工智能代理通过标准化接口调用外部工具。构建由 Scavio API 支持的自定义 MCP 服务器使您可以完全控制工具定义、响应格式和速率限制，同时保持与任何 MCP 客户端（Cursor、Claude Desktop、自定义代理）的兼容性。本教程使用搜索和提取工具构建 Node.js MCP 服务器。
前置条件
- Node.js 18+
- npm or pnpm
- 来自 scavio.dev 的 Scavio API 密钥
- 基本熟悉MCP协议
操作指南
步骤 1: 设置 MCP 服务器项目
使用 MCP SDK 和 HTTP 依赖项初始化项目。
Bash
# Create project
mkdir scavio-mcp-server && cd scavio-mcp-server
npm init -y
npm install @modelcontextprotocol/sdk
# Project structure:
# scavio-mcp-server/
# index.js
# package.json
# package.json - add type module:
# { "type": "module", "main": "index.js" }步骤 2: 使用搜索工具实现 MCP 服务器
使用调用 Scavio API 的搜索工具创建 MCP 服务器。
JavaScript
// index.js
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
const API_KEY = process.env.SCAVIO_API_KEY;
const SH = { 'x-api-key': API_KEY, 'Content-Type': 'application/json' };
const server = new McpServer({ name: 'scavio-search', version: '1.0.0' });
server.tool('search',
{ query: { type: 'string', description: 'Search query' },
platform: { type: 'string', description: 'Platform: google, reddit, youtube, amazon, walmart', default: 'google' },
country: { type: 'string', description: 'Country code', default: 'us' } },
async ({ query, platform, country }) => {
const body = { query, country_code: country || 'us' };
if (platform && platform !== 'google') body.platform = platform;
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH, body: JSON.stringify(body)
});
const data = await r.json();
const results = (data.organic_results || []).slice(0, 5)
.map(r => `${r.position}. ${r.title}\n ${r.link}\n ${(r.snippet || '').slice(0, 120)}`).join('\n\n');
return { content: [{ type: 'text', text: results || 'No results found.' }] };
}
);
console.error('Scavio MCP server starting...');步骤 3: 添加提取工具
在MCP服务器上添加URL内容提取工具。
JavaScript
// Add after the search tool definition:
server.tool('extract',
{ url: { type: 'string', description: 'URL to extract content from' } },
async ({ url }) => {
const r = await fetch('https://api.scavio.dev/api/v1/extract', {
method: 'POST', headers: SH, body: JSON.stringify({ url })
});
const data = await r.json();
const content = data.content || data.text || JSON.stringify(data);
return { content: [{ type: 'text', text: content.slice(0, 2000) }] };
}
);
// List available tools for debugging:
server.tool('list_platforms',
{},
async () => {
const platforms = ['google', 'youtube', 'amazon', 'walmart', 'reddit'];
const text = platforms.map(p => `- ${p}: Search ${p} results via Scavio API`).join('\n');
return { content: [{ type: 'text', text: `Available search platforms:\n${text}\n\nCost: $0.005 per search` }] };
}
);步骤 4: 启动服务器并配置客户端
启动 MCP 服务器并将其连接到 Cursor 或 Claude Desktop。
Bash
// Add at the end of index.js:
const transport = new StdioServerTransport();
await server.connect(transport);
// Run the server:
// SCAVIO_API_KEY=your_key node index.js
// Configure in Cursor (~/.cursor/mcp.json):
// {
// "mcpServers": {
// "scavio-custom": {
// "command": "node",
// "args": ["/path/to/scavio-mcp-server/index.js"],
// "env": { "SCAVIO_API_KEY": "your_key" }
// }
// }
// }
// Or in Claude Desktop (claude_desktop_config.json):
// {
// "mcpServers": {
// "scavio-custom": {
// "command": "node",
// "args": ["/path/to/scavio-mcp-server/index.js"],
// "env": { "SCAVIO_API_KEY": "your_key" }
// }
// }
// }Python 示例
Python
# Python MCP server alternative:
import os, requests
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def search(query, platform=None):
body = {'query': query, 'country_code': 'us'}
if platform: body['platform'] = platform
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json=body).json()
for r in data.get('organic_results', [])[:3]:
print(f'{r["position"]}. {r["title"][:50]}')
search('mcp server tutorial')JavaScript 示例
JavaScript
// Test the MCP server tools directly:
const API_KEY = process.env.SCAVIO_API_KEY;
const SH = { 'x-api-key': API_KEY, 'Content-Type': 'application/json' };
async function testSearch(query, platform) {
const body = { query, country_code: 'us' };
if (platform) body.platform = platform;
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH, body: JSON.stringify(body)
}).then(r => r.json());
(data.organic_results || []).slice(0, 3).forEach(r =>
console.log(`${r.position}. ${r.title.slice(0, 50)}`)
);
}
await testSearch('mcp server tutorial');预期输出
JSON
Scavio MCP server starting...
# In Cursor after configuration:
Cursor Settings > MCP:
scavio-custom: Connected (3 tools)
# Tools available:
- search: Multi-platform web search
- extract: URL content extraction
- list_platforms: Show available platforms
# Test query in Cursor Composer:
User: Search Reddit for MCP server best practices
Agent: [Calling scavio-custom.search with platform='reddit']
1. Building MCP Servers - Best Practices for 2026
https://reddit.com/r/...
Here are the key patterns I've found...
Cost per search: $0.005