How to Use the Basecamp MCP in OpenAI Agents SDK

Q: How do I connect Basecamp to the OpenAI Agents SDK?

Initialize MCPServerStreamableHttp with your Vinkius MCP endpoint. Pass it to the Agent constructor using the mcpservers array and set cacheToolsList=True.

Q: Can the OpenAI Agents SDK read Basecamp message boards?

Yes. Your agent runs listmessages to grab the board history and getmessage to read specific threads.

Q: Does the Basecamp MCP Server support agent handoffs?

It handles handoffs naturally. One agent might run createtodo while a specialized follow-up agent executes completetodo later.

Connect the Basecamp MCP Server to the OpenAI Agents SDK and ship production-ready task management workflows with built-in safety guardrails.

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Connect Basecamp MCP to OpenAI Agents SDK

Create your Vinkius account to connect Basecamp to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Basecamp with OpenAI Agents SDK

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Manage Basecamp Projects via MCP Server

The Basecamp MCP server gives your OpenAI agents direct control over project creation and team assignments. You pass a client name and a brief description, and the agent fires off `create_project` to spin up a new workspace. Tracing these actions through the OpenAI dashboard shows exactly which team members were added. Your agent runs `list_people` to grab IDs, then maps them into the new setup so everyone knows what to do next.

Automate Task Lifecycles

Your system handles the entire to-do lifecycle without human intervention. An agent spots an unhandled customer request, calls `create_todo` with a strict due date, and assigns it to the on-call engineer. Hand-offs between specialized agents work perfectly here. A triage agent creates the work item, while a separate QA agent monitors progress and triggers `complete_todo` once the validation tests pass.

Inject Context into OpenAI Agents SDK

Project discussions feed directly into your agent's context window. Instead of guessing why a decision was made, the agent runs `list_messages` to pull the actual thread from the message board. Guardrails prevent the agent from spamming the team. It reads the history using `get_message`, formulates a precise update, and executes `create_comment` to drop a single HTML-formatted note onto the recording.

Setup guide

Set up Basecamp MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Basecamp tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Basecamp tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Basecamp tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Basecamp Agent",
            instructions="You have access to Basecamp tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Basecamp MCP in OpenAI Agents SDK

Initialize `MCPServerStreamableHttp` with your Vinkius MCP endpoint. Pass it to the Agent constructor using the `mcp_servers` array and set `cacheToolsList=True`.

Yes. Your agent runs `list_messages` to grab the board history and `get_message` to read specific threads.

It handles handoffs naturally. One agent might run `create_todo` while a specialized follow-up agent executes `complete_todo` later.

Provide an array of person IDs when creating a task. Agents find these IDs by executing `list_project_people` before building the task payload.

We run the server inside an ephemeral V8 Isolate Sandbox. Every execution spins up a fresh environment that dies immediately after your agent fetches team profiles or posts comments, leaving zero persistent traces behind.

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