> ## Documentation Index > Fetch the complete documentation index at: https://docs.paypal.ai/llms.txt > Use this file to discover all available pages before exploring further. # 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](https://www.anthropic.com/) and [OpenAI](https://openai.com/), 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](https://developer.paypal.com/api/rest/#link-getclientidandclientsecret). ```bash cURL theme={null} 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" ``` ```python Python theme={null} 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"] ``` ```typescript TypeScript theme={null} 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](https://docs.anthropic.com/en/docs/agents-and-tools/mcp-connector). 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. ```bash cURL theme={null} 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" } ] }' ``` ```python Python theme={null} 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) ``` ```typescript TypeScript theme={null} 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](https://gist.github.com/stevenheidel/a6c8ed8ce1632dff1284e44a17554993) 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. ```bash cURL theme={null} 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." }' ``` ```python Python theme={null} 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) ``` ```typescript TypeScript theme={null} 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); })(); ```