How to Use the Sunsama MCP in LangChain

Build multi-step reasoning chains with the Sunsama MCP Server and LangChain.

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Works with every AI agent you already use

…and any MCP-compatible client

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Connect Sunsama MCP to LangChain

Create your Vinkius account to connect Sunsama to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Sunsama with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Orchestrate task creation across multiple sources.

Need to map out a complex project timeline? You can kick off new items using `create_task(text, plannedDate)`. This handles the core scheduling logic for your entire workflow. It's more than just logging tasks. If you need to update an existing item or delete it permanently with `delete_task`, LangChain builds these steps into a single chain call. The agent decides the full sequence.

Understand user and project context quickly.

Before making any changes, you need to know what's going on. Call `get_user_profile()` to grab details about who's running the process. You can also use `list_channels()` or `list_contexts()` to map out available workspaces. These foundational calls give the agent the necessary scope. When building a multi-step chain, knowing these boundaries is critical for accurate task management.

Manage and filter your entire task list.

You can get a full picture of everything by calling `list_tasks()`. This tool lets you narrow the focus by filtering tasks with specific dates. It’s great for planning sprints. If you need to check on one item specifically, run `get_task_details(taskId)`. These tools allow LangChain agents to gather data, decide what's missing, and then execute the necessary write operations.

Setup guide

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

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Common questions about Sunsama MCP in LangChain

LangChain treats every MCP tool call as a link in your reasoning chain. The agent decides which specific tool—like `update_task` or `list_tasks`—to call and what order to use it in, based on the results of previous steps.

Absolutely. You can build entire multi-step pipelines where an agent might first run `list_channels` and then, using that output, decide to call `create_task`. The whole process is traceable via your chain's execution path.

This MCP Server touches task text and planned dates. When building agents, always ensure the intermediate results of tasks—the actual content retrieved from `get_task_details`—are handled according to your security protocols.

It's strong for task orchestration. Because it exposes tools like `update_task` and `create_task`, you can write agents that actively manage the state of your projects, not just read from them.

Start by listing available resources. Use a chain that calls `list_contexts` first, then pass those contexts into an agent designed to call `get_task_details`. This validates your connection quickly.

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