How to Use the Xiaoshouyi / Neocrm MCP in LangChain

Build multi-step workflows for Xiaoshouyi / Neocrm using LangChain.

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Connect Xiaoshouyi / Neocrm MCP to LangChain

Create your Vinkius account to connect Xiaoshouyi / Neocrm 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 Xiaoshouyi / Neocrm with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate lead qualification chains.

You can build a full workflow: first, use `list_leads` to find promising accounts. Next, your agent calls `get_lead` for deep details on specific records. From there, the chain decides if an opportunity is viable and uses `create_opportunity` to log it directly into Neocrm.

Manage full sales account lifecycles.

Need to audit a client? The agent can first call `list_accounts` for a broad view. Then, it uses `list_contacts` and `get_lead` to pull specific contact info and lead statuses. This chain ensures every piece of data—from the account list to individual contacts—is gathered in one sequence.

Schedule and track internal tasks.

The system can check out who's available by calling `list_users`. Then, it uses `list_tasks` to see pending items for a specific team member. Finally, it can set up the next steps with `create_task`. This makes sure complex internal planning runs automatically through your agent.

Setup guide

Set up Xiaoshouyi / Neocrm 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 Xiaoshouyi / Neocrm 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({
    "xiaoshouyi-neocrm-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 Xiaoshouyi / Neocrm 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 Xiaoshouyi / Neocrm. 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 Xiaoshouyi / Neocrm MCP in LangChain

LangChain lets you chain multiple actions. For instance, you can first retrieve a list of contacts using `list_contacts` and then feed that data into an agent to automatically create follow-up tasks via `create_task`.

This MCP Server handles user, contact, lead, and opportunity data. When using LangChain, your agent is actively reading and writing these record types to manage the sales cycle.

Absolutely. The strength of this setup is that every tool call, like `get_lead` or `list_opportunities`, acts as a step in your larger, multi-stage decision process.

You'll use it when you need complex coordination. For example, an agent could list all overdue tasks (`list_tasks`) and then automatically create a new high-priority follow-up task (`create_task`).

The server handles sensitive records like contact details, lead information, and account names. The agent must be configured with explicit permissions to read or write these specific types of customer relationship data.

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