AI Agent Quickstart

In this guide, a model answers “What issues are assigned to me?” using the user’s own Linear account. The model calls Linear’s MCP tools through mcp.bundleup.io. BundleUp attaches the user’s token, so neither the model nor your code handles it. Your backend tells the model provider to use mcp.bundleup.io as an MCP server, with your API key and the user’s connection ID as the credential. The model provider calls BundleUp, BundleUp swaps in the user’s Linear token and forwards to Linear’s MCP server, and the results flow back to the model.

Prerequisites

A BundleUp workspace with a Linear integration (identifier linear), an API key, and a registered redirect URI. Follow steps 1 to 3 of the Quickstart, choosing Linear instead of GitHub.
An OpenAI or Anthropic API key.

MCP works for providers that run their own MCP server. Each provider’s page at bundleup.io/integrations shows whether it does.

1. Connect the user’s account

Run the authorization flow for the linear integration and store the resulting connection_id against your user. For a quick test, follow steps 4 and 5 of the Quickstart with integration_id=linear.

2. Check the connection works

Before involving a model, list the tools the connection can see:

curl -s https://mcp.bundleup.io \
  -H "Authorization: Bearer $BUNDLEUP_API_KEY" \
  -H "BU-Connection-Id: conn_abc123" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

You should get Linear’s tool list back. A 400 mcp_not_supported means the provider has no MCP server; use the Proxy for it instead.

3. Give the connection to a model

OpenAI and Anthropic can call an MCP server themselves. They accept a single credential, so BundleUp accepts the API key and connection ID joined with a dot: API_KEY.CONNECTION_ID. The SDK’s hosted() builds that for you.

import OpenAI from 'openai';
import { BundleUp } from '@bundleup/sdk';

const openai = new OpenAI();
const bundleup = new BundleUp(process.env.BUNDLEUP_API_KEY);

// Look this up for the signed-in user
const { url, token } = bundleup.mcp(user.linearConnectionId).hosted();

const response = await openai.responses.create({
  model: 'gpt-4o',
  input: 'What issues are assigned to me?',
  tools: [
    {
      type: 'mcp',
      server_label: 'linear',
      server_url: url,
      authorization: token,
      require_approval: 'never',
    },
  ],
});

console.log(response.output_text);

import Anthropic from '@anthropic-ai/sdk';
import { BundleUp } from '@bundleup/sdk';

const anthropic = new Anthropic();
const bundleup = new BundleUp(process.env.BUNDLEUP_API_KEY);

const { url, token } = bundleup.mcp(user.linearConnectionId).hosted();

const message = await anthropic.beta.messages.create({
  model: 'claude-opus-5-5',
  max_tokens: 16000,
  betas: ['mcp-client-2025-11-20'],
  mcp_servers: [
    { type: 'url', url, name: 'linear', authorization_token: token },
  ],
  // Every server in mcp_servers needs a matching mcp_toolset entry
  tools: [{ type: 'mcp_toolset', mcp_server_name: 'linear' }],
  messages: [{ role: 'user', content: 'What issues are assigned to me?' }],
});

from openai import OpenAI
from bundleup import BundleUp

openai = OpenAI()
bundleup = BundleUp(os.environ["BUNDLEUP_API_KEY"])

hosted = bundleup.mcp(user.linear_connection_id).hosted()

response = openai.responses.create(
    model="gpt-4o",
    input="What issues are assigned to me?",
    tools=[{
        "type": "mcp",
        "server_label": "linear",
        "server_url": hosted["url"],
        "authorization": hosted["token"],
        "require_approval": "never",
    }],
)
print(response.output_text)

With model-hosted MCP, your BundleUp API key is sent to the model provider, whose servers make the MCP calls. If that isn’t acceptable, run an MCP client in your own backend instead. See Using MCP clients.

4. Serve many users

The only per-user input is the connection ID. Look it up for whoever is chatting, and pass it in. Two users get two different Linear accounts from the same code. To give one agent several providers for the same user, add one MCP server entry per connection (for example, one for Linear and one for Slack). See Agents for many users.

Next steps

How Proxy MCP works: headers, sessions, streaming and errors.
Using MCP clients: run the MCP client in your own backend, or connect from Claude Code.
Choosing scopes: the OAuth scopes you grant decide which tools the agent can use.

Get started

Concepts

Authentication

Integrations

Proxy

Unified API

MCP

Webhooks

Operations

Platform

AI Agent Quickstart

Prerequisites

1. Connect the user’s account

2. Check the connection works

3. Give the connection to a model

4. Serve many users

Next steps

​Prerequisites

​1. Connect the user’s account

​2. Check the connection works

​3. Give the connection to a model

​4. Serve many users

​Next steps

Prerequisites

1. Connect the user’s account

2. Check the connection works

3. Give the connection to a model

4. Serve many users

Next steps