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How to Use Model Context Protocol (MCP)

Last verified 24 Aug 2026

Inference provides a single control plane for managing inference workflows. It includes a Model Catalog where you can view available foundation models, including both DigitalOcean-hosted and third-party commercial models, compare model capabilities and pricing, use routing to match inference requests to the best-fit model, and run inference using serverless or dedicated deployments.

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MCP servers expose tools that the model can call, such as fetching account data, managing schedules, or interacting with third-party APIs. The MCP built-in tool connects the model to remote Model Context Protocol (MCP) servers and orchestrates calls to them.

Connect to an Authenticated MCP Server

You can connect to authenticated MCP servers using bearer token authentication. The following example sends a Chat Completions request that connects to the DigitalOcean Accounts MCP server. Replace $DIGITALOCEAN_API_TOKEN with a valid DigitalOcean personal access token.

Python

import os
from pydo.inference import Client

client = Client(token=os.environ.get("MODEL_ACCESS_KEY"))

resp = client.chat.completions.create(
    model="openai-gpt-4o",
    messages=[
        {"role": "user", "content": "Fetch my DigitalOcean account information and summarize it in 2 bullets."},
    ],
    tools=[
        {
            "type": "mcp",
            "server_label": "digitalocean",
            "server_url": "https://accounts.mcp.digitalocean.com/mcp",
            "authorization": f"Bearer {os.environ.get('DIGITALOCEAN_API_TOKEN')}",
            "allowed_tools": ["account-get-information"],
        }
    ],
    tool_choice="required",
    stream=False,
    max_tokens=512,
)

print(resp.choices[0].message.content)

JavaScript

import { InferenceClient } from "@digitalocean/dots";

const client = new InferenceClient({
    apiKey: process.env.MODEL_ACCESS_KEY,
});

const completion = await client.chat.completions.create({
    model: "openai-gpt-4o",
    messages: [
        { role: "user", content: "Fetch my DigitalOcean account information and summarize it in 2 bullets." },
    ],
    tools: [
        {
            type: "mcp",
            server_label: "digitalocean",
            server_url: "https://accounts.mcp.digitalocean.com/mcp",
            authorization: `Bearer ${process.env.DIGITALOCEAN_API_TOKEN}`,
            allowed_tools: ["account-get-information"],
        },
    ],
    tool_choice: "required",
    stream: false,
    max_tokens: 512,
});

console.log(completion.choices[0].message.content);

cURL

curl -X POST https://inference.do-ai.run/v1/chat/completions \
  -H "Authorization: Bearer $MODEL_ACCESS_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai-gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": "Fetch my DigitalOcean account information and summarize it in 2 bullets."
      }
    ],
    "tools": [
      {
        "type": "mcp",
        "server_label": "digitalocean",
        "server_url": "https://accounts.mcp.digitalocean.com/mcp",
        "authorization": "Bearer $DIGITALOCEAN_API_TOKEN",
        "allowed_tools": ["account-get-information"]
      }
    ],
    "tool_choice": "required",
    "stream": false,
    "max_tokens": 512
  }'

The allowed_tools array restricts which tools from the MCP server the model can call. In this example, only the account-get-information tool is available. When omitted, the model can use any tool the server exposes. For the full set of MCP tool parameters, see the Serverless Inference API reference. The response looks like the following:

{
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null,
            "message": {
                "annotations": [
                    {
                        "type": "tool_use",
                        "tool_use": {
                            "name": "digitalocean__account-get-information",
                            "call_id": "call_xasb9Xk1HAfT564P3bteZZTT",
                            "arguments": "{}",
                            "status": "completed",
                            "output": "{\n  \"droplet_limit\": 100,\n  \"floating_ip_limit\": 75,\n  \"reserved_ip_limit\": 75,\n  \"volume_limit\": 5000,\n  \"email\": \"[email protected]\",\n  \"name\": \"dev-sammy\",\n  \"uuid\": \"de55ee97-21ab-452d-aaf0-d4046480xxxx\",\n  \"email_verified\": true,\n  \"status\": \"active\",\n  \"team\": {\n    \"name\": \"My Team\",\n    \"uuid\": \"de55ee97-21ab-452d-aaf0-d4046480xxxx\"\n  }\n}"
                        }
                    }
                ],
                "content": "- Your account (\"dev-sammy\") is active with email \"[email protected]\", which is verified. You are part of \"My Team\" with a UUID of \"de55ee97-21ab-452d-aaf0-d4046480xxxx\".\n- You have a resource allocation limit of 100 droplets, 75 floating IPs, 75 reserved IPs, and 5000 volumes.",
                "reasoning_content": null,
                "refusal": null,
                "role": "assistant"
            }
        }
    ],
....    }
}

Connect to an Unauthenticated MCP Server

You can also connect to public MCP servers that do not require authentication. The following example sends a Responses API request: