서버에서 직접 호출

// npm install openai
import OpenAI from 'openai';

const MCP_URL = 'https://api.xdata.kr/mcp';
const AUTH = Buffer.from('CLIENT_ID:CLIENT_SECRET').toString('base64');

async function mcpPost(method, params = {}) {
  const res = await fetch(MCP_URL, {
    method: 'POST',
    headers: {
      Authorization: `Basic ${AUTH}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({ jsonrpc: '2.0', id: Date.now(), method, params }),
  });
  return res.json();
}

// 1. tools/list → OpenAI tools 포맷으로 변환
const { result } = await mcpPost('tools/list');
const tools = result.tools.map(t => ({
  type: 'function',
  function: { name: t.name, description: t.description, parameters: t.inputSchema },
}));

// 2. OpenAI 호출 (Gemini, Azure, OpenAI 호환 모두 동일)
const openai = new OpenAI(/* baseURL, apiKey 등 필요 시 추가 */);
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  tools,
  messages: [{ role: 'user', content: '질문 입력' }],
});

// 3. tool_calls → XBOSS 실행
for (const call of response.choices[0].message.tool_calls ?? []) {
  const { result: res } = await mcpPost('tools/call', {
    name: call.function.name,
    arguments: JSON.parse(call.function.arguments),
  });
  console.log(res);
}

# pip install openai requests
import base64, json, requests
from openai import OpenAI

MCP_URL = 'https://api.xdata.kr/mcp'
AUTH = base64.b64encode(b'CLIENT_ID:CLIENT_SECRET').decode()
HEADERS = {
    'Authorization': f'Basic {AUTH}',
    'Content-Type': 'application/json',
}

def mcp_post(method, params=None):
    body = {'jsonrpc': '2.0', 'id': 1, 'method': method, 'params': params or {}}
    return requests.post(MCP_URL, headers=HEADERS, json=body).json()

# 1. tools/list → OpenAI tools 포맷으로 변환
result = mcp_post('tools/list')['result']
tools = [
    {'type': 'function', 'function': {
        'name': t['name'], 'description': t['description'], 'parameters': t['inputSchema']
    }}
    for t in result['tools']
]

# 2. OpenAI 호출 (Gemini, Azure, OpenAI 호환 모두 동일)
client = OpenAI()  # base_url, api_key 필요 시 추가
response = client.chat.completions.create(
    model='gpt-4o',
    tools=tools,
    messages=[{'role': 'user', 'content': '질문 입력'}],
)

# 3. tool_calls → XBOSS 실행
for call in response.choices[0].message.tool_calls or []:
    res = mcp_post('tools/call', {
        'name': call.function.name,
        'arguments': json.loads(call.function.arguments),
    })
    print(res['result'])

// npm install @anthropic-ai/sdk
import Anthropic from '@anthropic-ai/sdk';

const MCP_URL = 'https://api.xdata.kr/mcp';
const AUTH = Buffer.from('CLIENT_ID:CLIENT_SECRET').toString('base64');

async function mcpPost(method, params = {}) {
  const res = await fetch(MCP_URL, {
    method: 'POST',
    headers: {
      Authorization: `Basic ${AUTH}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({ jsonrpc: '2.0', id: Date.now(), method, params }),
  });
  return res.json();
}

// 1. tools/list → Anthropic tools 포맷으로 변환
const { result } = await mcpPost('tools/list');
const tools = result.tools.map(t => ({
  name: t.name,
  description: t.description,
  input_schema: t.inputSchema,
}));

// 2. Anthropic 호출
const client = new Anthropic();
const response = await client.messages.create({
  model: 'claude-opus-5',
  max_tokens: 16000,
  tools,
  messages: [{ role: 'user', content: '질문 입력' }],
});

// 3. tool_use → XBOSS 실행
for (const block of response.content) {
  if (block.type === 'tool_use') {
    const { result: res } = await mcpPost('tools/call', {
      name: block.name,
      arguments: block.input,
    });
    console.log(res);
  }
}

# pip install anthropic requests
import base64, requests
import anthropic

MCP_URL = 'https://api.xdata.kr/mcp'
AUTH = base64.b64encode(b'CLIENT_ID:CLIENT_SECRET').decode()
HEADERS = {
    'Authorization': f'Basic {AUTH}',
    'Content-Type': 'application/json',
}

def mcp_post(method, params=None):
    body = {'jsonrpc': '2.0', 'id': 1, 'method': method, 'params': params or {}}
    return requests.post(MCP_URL, headers=HEADERS, json=body).json()

# 1. tools/list → Anthropic tools 포맷으로 변환
result = mcp_post('tools/list')['result']
tools = [
    {'name': t['name'], 'description': t['description'], 'input_schema': t['inputSchema']}
    for t in result['tools']
]

# 2. Anthropic 호출
client = anthropic.Anthropic()
response = client.messages.create(
    model='claude-opus-5',
    max_tokens=16000,
    tools=tools,
    messages=[{'role': 'user', 'content': '질문 입력'}],
)

# 3. tool_use → XBOSS 실행
for block in response.content:
    if block.type == 'tool_use':
        res = mcp_post('tools/call', {'name': block.name, 'arguments': block.input})
        print(res['result'])