--- title: How to Use Model Context Protocol (MCP) description: Connect DigitalOcean Inference requests to remote Model Context Protocol (MCP) servers so the model can call tools those servers expose. product: Inference url: https://docs.digitalocean.com/products/inference/how-to/use-server-side-tools/use-mcp/ last_updated: "2026-08-24" --- > **For AI agents:** The documentation index is at [https://docs.digitalocean.com/llms.txt](https://docs.digitalocean.com/llms.txt). Markdown versions of pages use the same URL with `index.html.md` in place of the HTML page (for example, append `index.html.md` to the directory path instead of opening the HTML document). # How to Use Model Context Protocol (MCP) 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. 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)](https://modelcontextprotocol.io/) 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](https://docs.digitalocean.com/reference/api/create-personal-access-token/index.html.md). ### Python ```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 ```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 ```shell 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](https://docs.digitalocean.com/products/inference/reference/api/serverless-inference/index.html.md). The response looks like the following: ```text { "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\": \"dev-sammy@example.com\",\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 \"dev-sammy@example.com\", 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: ### Python ```python import os from pydo.inference import Client client = Client(token=os.environ.get("MODEL_ACCESS_KEY")) resp = client.responses.create( model="openai-gpt-4o", input="Create a scheduling poll called Team Lunch with two time options for tomorrow at noon and the day after at noon.", tools=[ { "type": "mcp", "server_label": "timergy", "server_url": "https://api.timergy.com/mcp", } ], tool_choice="required", stream=False, max_output_tokens=512, ) print(resp) ``` ### JavaScript ```javascript import { InferenceClient } from "@digitalocean/dots"; const client = new InferenceClient({ apiKey: process.env.MODEL_ACCESS_KEY, }); const resp = await client.responses.create({ model: "openai-gpt-4o", input: "Create a scheduling poll called Team Lunch with two time options for tomorrow at noon and the day after at noon.", tools: [ { type: "mcp", server_label: "timergy", server_url: "https://api.timergy.com/mcp", }, ], tool_choice: "required", stream: false, max_output_tokens: 512, }); console.log(resp); ``` ### cURL ```shell curl -X POST https://inference.do-ai.run/v1/responses \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "openai-gpt-4o", "input": "Create a scheduling poll called Team Lunch with two time options for tomorrow at noon and the day after at noon.", "tools": [ { "type": "mcp", "server_label": "timergy", "server_url": "https://api.timergy.com/mcp" } ], "tool_choice": "required", "stream": false, "max_output_tokens": 512 }' ``` The response looks like the following: ```text { ... "model": "openai-gpt-4o", "object": "response", "output": [ { "arguments": "{\"autoFinalize\":false,\"creatorName\":\"Assistant for Team\",\"deadline\":\"2026-04-08T12:00:00-05:00\",\"description\":\"Scheduling poll for a team lunch\",\"invitees\":[],\"location\":\"Office Cafeteria\",\"options\":[{\"end\":\"2026-04-10T13:00:00-05:00\",\"start\":\"2026-04-10T12:00:00-05:00\"},{\"end\":\"2026-04-11T13:00:00-05:00\",\"start\":\"2026-04-11T12:00:00-05:00\"}],\"title\":\"Team Lunch\"}", "call_id": "call_uallF71f2THNYsEvZUubSUhQ", "name": "timergy__create_poll", "status": "completed", "type": "function_call" }, { "call_id": "call_uallF71f2THNYsEvZUubSUhQ", "output": "{\n \"pollId\": \"fdc8110b-274d-45b4-b791-0149f6cfc4bc\",\n \"title\": \"Team Lunch\",\n \"url\": \"https://timergy.com/en/polls/fdc8110b-274d-45b4-b791-0149f6cfc4bc\",\n \"passphrase\": \"horse-sword-thumb\",\n \"options\": [\n {\n \"id\": \"c8879755-9136-497d-aedc-81b50fafbb13\",\n \"start\": \"2026-04-10T17:00:00.000Z\",\n \"end\": \"2026-04-10T18:00:00.000Z\",\n \"label\": null\n },\n {\n \"id\": \"58c474c6-12b4-42d5-a96e-c6c977d8a3b2\",\n \"start\": \"2026-04-11T17:00:00.000Z\",\n \"end\": \"2026-04-11T18:00:00.000Z\",\n \"label\": null\n }\n ],\n \"expiresAt\": \"2026-04-21T18:00:00.000Z\",\n \"autoFinalize\": false,\n \"inviteesSent\": 0,\n \"note\": \"Share the URL with participants. The passphrase is saved for finalization. Assistant for Team's \\\"yes\\\" votes have been auto-submitted.\"\n}", "status": "completed", "type": "function_call_output" }, { "content": [ { "annotations": [], "logprobs": [], "text": "The scheduling poll \"Team Lunch\" has been created. You can share the following URL with participants to vote:\n\n**Poll URL:** [Team Lunch Poll](https://timergy.com/en/polls/fdc8110b-274d-45b4-b791-0149f6cfc4bc)\n\nFor admin access and to finalize the poll, you can use the passphrase:\n\n**Passphrase:** `horse-sword-thumb`\n\nThe poll includes two time slot options:\n- April 10, 2026, from 12:00 PM to 1:00 PM (local time)\n- April 11, 2026, from 12:00 PM to 1:00 PM (local time)", "type": "output_text" } ], ... "tool_choice": "auto", "tools": [ { "type": "mcp", "server_label": "timergy", "server_url": "https://api.timergy.com/mcp" } ], ... } } ```