LLM integration quickstart guide

PayPal recently launched a Model Context Protocol (MCP) server that customers can use to access the power of PayPal using natural language with any AI agent. PayPal’s remote MCP server now supports large language models (LLMs) from Anthropic and OpenAI, which brings even greater flexibility.

Before you begin

Before you begin, generate a PayPal access token. You can generate an access token by making a POST request to PayPal’s token endpoint, or you can generate it programmatically. For more information, see Get client ID and client secret.

curl -X POST "https://api-m.paypal.com/v1/oauth2/token" \
  -u "$PAYPAL_CLIENT_ID:$PAYPAL_CLIENT_SECRET" \
  -H "Accept: application/json" \
  -H "Accept-Language: en_US" \
  -d "grant_type=client_credentials"

response = requests.post(
    "https://api-m.paypal.com/v1/oauth2/token",
    headers={
        "Accept": "application/json",
        "Accept-Language": "en_US",
    },
    data={"grant_type": "client_credentials"},
    auth=HTTPBasicAuth(client_id, client_secret)  # Basic Auth
)
access_token = response.json()["access_token"]

const clientId = process.env.PAYPAL_CLIENT_ID!;
const clientSecret = process.env.PAYPAL_CLIENT_SECRET!;
const auth = Buffer.from(`${clientId}:${clientSecret}`).toString('base64');

(async () => {
  const response = await fetch('https://api-m.paypal.com/v1/oauth2/token', {
    method: 'POST',
    headers: {
      'Accept': 'application/json',
      'Accept-Language': 'en_US',
      'Content-Type': 'application/x-www-form-urlencoded',
      'Authorization': `Basic ${auth}`,
    },
    body: 'grant_type=client_credentials',
  });

  if (!response.ok) {
    throw new Error(`HTTP error! status: ${response.status}`);
  }

  const data = await response.json();
  const accessToken = data.access_token;
  console.log(accessToken);
})();

Anthropic integration

When you have your access token, you can connect the remote MCP server with the Anthropic LLM using the steps on this page from Anthropic. For more information about connecting to PayPal’s remote MCP server, see the MCP server quickstart guide. After you connect, you can start using Anthropic’s LLM with PayPal’s MCP server tools by initializing the client and making a request. For example, you could ask it to create an invoice, as shown in the following code examples.

curl https://api.anthropic.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "anthropic-beta: mcp-client-2025-04-04" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1000,
    "messages": [{"role": "user", "content": "Create an invoice for john.doe@example.com for 2 hours of consulting services at the rate of $150 per hour."}],
    "mcp_servers": [
      {
        "type": "url",
        "url": "https://mcp.paypal.com/sse",
        "name": "example-mcp",
        "authorization_token": "YOUR_TOKEN"
      }
    ]
  }'

import os
import requests

api_key = os.environ["ANTHROPIC_API_KEY"]
url = "https://api.anthropic.com/v1/messages"
payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1000,
    "messages": [{
        "role": "user",
        "content": "Create an invoice for john.doe@example.com for 2 hours of consulting services at the rate of $150 per hour."
    }],
    "mcp_servers": [{
        "type": "url",
        "url": "https://mcp.paypal.com/sse",
        "name": "example-mcp",
        "authorization_token": "YOUR_TOKEN"
    }]
}
headers = {
    "Content-Type": "application/json",
    "X-API-Key": api_key,
    "anthropic-version": "2023-06-01",
    "anthropic-beta": "mcp-client-2025-04-04"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)

const apiKey = process.env.ANTHROPIC_API_KEY!;
const url = "https://api.anthropic.com/v1/messages";

const payload = {
  model: "claude-sonnet-4-20250514",
  max_tokens: 1000,
  messages: [
    {
      role: "user",
      content: "Create an invoice for john.doe@example.com for 2 hours of consulting services at the rate of $150 per hour.",
    },
  ],
  mcp_servers: [
    {
      type: "url",
      url: "https://mcp.paypal.com/sse",
      name: "example-mcp",
      authorization_token: "YOUR_TOKEN",
    },
  ],
};

(async () => {
  const response = await fetch(url, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "X-API-Key": apiKey,
      "anthropic-version": "2023-06-01",
      "anthropic-beta": "mcp-client-2025-04-04",
    },
    body: JSON.stringify(payload),
  });

  if (!response.ok) {
    throw new Error(`HTTP error! status: ${response.status}`);
  }

  const data = await response.json();
  console.log(data);
})();

OpenAI integration

For additional information about the OpenAI side of this integration, see this post from OpenAI.

When you have your access token, you can connect to the MCP server. For details about connecting to PayPal’s remote MCP server, see the MCP server quickstart guide in PayPal’s developer documentation.
After you connect, you can start using OpenAI with PayPal’s MCP server tools by initializing the client and making a request. For example, you can ask the client to create an invoice, as shown in the following code examples.

curl -X POST https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4.1",
    "tools": [
      {
        "type": "mcp",
        "server_label": "paypal-mcp",
        "server_url": "https://mcp.paypal.com/sse",
        "require_approval": "never",
        "headers": { "Authorization": "Bearer $PAYPAL_ACCESSTOKEN" }
      }
    ],
    "input": "Create an invoice for john.doe@example.com for 2 hours of consulting services at the rate of $150 per hour."
  }'

import os
import requests

api_key = os.environ["OPENAI_API_KEY"]
url = "https://api.openai.com/v1/chat/completions"
payload = {
    "model": "gpt-4.1",
    "tools": [
        {
            "type": "mcp",
            "server_label": "paypal-mcp",
            "server_url": "https://mcp.paypal.com/sse"
        }
    ],
    "messages": [
        {
            "role": "user",
            "content": "Create an invoice for john.doe@example.com for 2 hours of consulting services at the rate of $150 per hour."
        }
    ]
}
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {api_key}"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)

const apiKey = process.env.OPENAI_API_KEY!;
const url = "https://api.openai.com/v1/chat/completions";

const payload = {
  model: "gpt-4.1",
  tools: [
    {
      type: "mcp",
      server_label: "paypal-mcp",
      server_url: "https://mcp.paypal.com/sse",
    },
  ],
  messages: [
    {
      role: "user",
      content: "Create an invoice for john.doe@example.com for 2 hours of consulting services at the rate of $150 per hour.",
    },
  ],
};

(async () => {
  const response = await fetch(url, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${apiKey}`,
    },
    body: JSON.stringify(payload),
  });

  if (!response.ok) {
    throw new Error(`HTTP error! status: ${response.status}`);
  }

  const data = await response.json();
  console.log(data);
})();

How tos

Tools

LLM integration quickstart guide

Before you begin

Anthropic integration

OpenAI integration

​Before you begin

​Anthropic integration

​OpenAI integration

Before you begin

Anthropic integration

OpenAI integration