How to Use the NeetoCal MCP in LangChain

Q: How do I manage multiple calendars with LangChain?

You use listcalendars to pull all connected accounts. The agent then selects the correct ID for the getslots call based on the user's specific request.

Chain NeetoCal tools in LangChain pipelines to automate your entire booking flow without writing custom glue code.

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Connect NeetoCal MCP to LangChain

Create your Vinkius account to connect NeetoCal 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 NeetoCal with LangChain

Ask AI about this MCP

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Claude

Perplexity

Automate booking workflows in LangChain

Chain `get_slots` with `create_booking` to let your agent manage appointments end-to-end. Your agent evaluates slot availability and executes the booking based on user intent. This setup keeps your logic inside the chain. You see every step in LangSmith, ensuring the agent makes the right call at every stage.

Dynamic scheduling decisions

Use `list_scheduling_links` to fetch custom meeting types before checking availability. The agent decides which link fits the user's request, keeping your calendar organized. This MCP Server provides the raw data your agents need. It removes the guesswork by feeding real-time availability directly into your reasoning loop.

Maintain stateful booking sessions

Initialize a client session to track multi-turn conversations. Your agent uses `get_booking` to confirm details or `reschedule_booking` if the user changes their mind. This keeps the context alive across multiple prompts. You don't lose track of the conversation when the agent switches between different booking tasks.

Setup guide

Set up NeetoCal 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 NeetoCal 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({
    "neetocal-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 NeetoCal 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 NeetoCal. 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 NeetoCal MCP in LangChain

You install the mcp-adapters package and point the client to your Vinkius endpoint. After that, you call get_tools and pass them directly into your LangChain agent constructor.

Yes. Since every tool call is a chain link, you can add error-handling nodes to catch failed `create_booking` attempts. This lets your agent retry or ask the user for clarification.

It does. Because the tools integrate directly into the chain, all inputs and outputs appear in your LangSmith traces. You can debug exactly why a specific booking was made.

You use `list_calendars` to pull all connected accounts. The agent then selects the correct ID for the `get_slots` call based on the user's specific request.

Only the data required for the tools to function. This includes booking IDs, email addresses, and slot times, all handled within the isolated Vinkius sandbox.

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