How to Use the ClickUp MCP in LangChain

Q: How do I connect ClickUp to my LangChain agent?

Install the langchain-mcp-adapters package first. Then initialize a MultiServerMCPClient pointing to your Vinkius endpoint URL. Grab the tools with client.gettools() and pass them into your agent constructor.

Q: How do I keep context across multiple tool calls?

Use client.session() when setting up your connection. This maintains persistent state while the agent navigates through workspaces, spaces, and folders.

Connect ClickUp to your LangChain agents. Build custom pipelines that read project data and create tasks based on multi-step reasoning.

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Connect ClickUp MCP to LangChain

Create your Vinkius account to connect ClickUp 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 ClickUp with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain ClickUp Tasks via MCP Server

Start by pulling your organizational structure. Your agent calls `list_clickup_workspaces` to find the right team, then drills down using `list_workspace_spaces` and `list_space_lists`. This gives the LLM the exact context it needs before making any changes. Everything links together. The output from finding a specific list feeds directly into `create_new_task`. You track the exact latency and token usage for these API calls in LangSmith while your agent works through the project hierarchy.

Read Project Status Automatically

Stop manually checking for updates. A ReAct agent runs `list_list_tasks` to grab the current sprint backlog. It reads the raw JSON response and decides what needs attention. When a specific item looks stuck, the chain triggers `get_task_details` to pull comments, assignees, and custom fields. The model processes that data and generates a status report without you opening a browser tab.

Build Context-Aware Workflows

Security and identity matter when updating project boards. The `get_my_clickup_profile` tool verifies exactly who the agent is acting as. Identity verification prevents accidental assignments to the wrong user account. Folder navigation works the same way. By chaining `list_space_folders` with other tools, you build a pipeline that maps out complex directory structures. This ClickUp MCP Server handles the routing while you focus on the agent logic.

Setup guide

Set up ClickUp 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 ClickUp 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({
    "clickup-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 ClickUp transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ClickUp. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ClickUp MCP in LangChain

Install the `langchain-mcp-adapters` package first. Then initialize a `MultiServerMCPClient` pointing to your Vinkius endpoint URL. Grab the tools with `client.get_tools()` and pass them into your agent constructor.

Yes. Because this runs through standard tool calling, LangSmith automatically logs every interaction. You see the exact inputs sent to the task creation endpoint and the raw JSON returned.

You get full access to the creation endpoint. The agent uses the specific list ID it found earlier to generate new items. It handles the schema mapping automatically.

Use `client.session()` when setting up your connection. This maintains persistent state while the agent navigates through workspaces, spaces, and folders.

Vinkius runs this MCP connector inside an ephemeral V8 Isolate sandbox. Your workspace structures and task details never touch a persistent disk. We handle the authentication via a single zero-trust token, meaning the agent only sees what you explicitly authorize.

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