How to Use the Basecamp MCP in LangChain

Q: Does this Basecamp MCP Server integration support LangChain's LangGraph framework?

Yes, you can pass the tools from client.gettools() directly into a LangGraph state graph. This lets your graph-based agent decide when to query listtodolists based on the current conversation state.

Build LangChain agents that inspect Basecamp message boards and update to-do lists based on live team updates.

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Connect Basecamp MCP to LangChain

Create your Vinkius account to connect Basecamp to LangChain 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 LangChain

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Sync Basecamp To-Dos with LangChain Chains

The `list_todos` MCP tool fetches active tasks from your project so your LangChain agent can analyze outstanding work items. By feeding these tasks directly into a sequential chain, the agent evaluates project blockages without manual data entry. You connect this tool to `list_todolists` to map out your entire workspace structure. LangSmith traces each tool call, revealing exactly how the model navigates your lists to update project statuses.

Run LangChain Multi-Step Basecamp Message Audits

The `list_messages` tool pulls board posts directly into your agent's reasoning loop. Your chain reads these messages, extracts action items, and immediately cross-references them against existing project files. This MCP Server allows your agent to chain this data with `list_people` to assign tasks to the right team members. You can inspect the execution flow in LangSmith to track token usage and tool latency for every message processed.

Trace Campfire Logs via LangChain Agents

The `list_campfire_lines` tool retrieves real-time team chat logs to keep your LangChain pipeline updated on urgent discussions. Instead of manually checking chat rooms, your agent monitors these logs to detect critical blockers. Combining this with `get_project` gives the agent full context on which workspace the chat lines belong to. The resulting MCP pipeline runs autonomously, feeding live chat updates directly into your LangChain decision trees.

Setup guide

Set up Basecamp MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Basecamp tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "basecamp-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Basecamp transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Basecamp MCP in LangChain

You provide your Vinkius endpoint token inside the MultiServerMCPClient configuration. Vinkius handles the Basecamp OAuth handshake behind the scenes, so your LangChain agent connects instantly without managing API keys.

This specific Basecamp toolset focuses on read operations like list_todos and list_messages. Your LangChain agent can read, analyze, and chain these outputs, but cannot write new tasks or messages back yet.

You use LangSmith tracing by wrapping the MCP client tools. Every call to list_campfire_lines or list_projects is logged with exact latency, input parameters, and token costs.

Yes, you can pass the tools from client.get_tools() directly into a LangGraph state graph. This lets your graph-based agent decide when to query list_todolists based on the current conversation state.

Vinkius runs the server in an isolated V8 sandbox that only accesses your Basecamp projects, to-dos, and chat lines during the active session. No project data or campfire logs are stored on Vinkius servers after the execution ends.

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