How to Use the Clientify MCP in LangChain

Build multi-step reasoning chains in LangChain that pull live contact data and deal status directly from your Clientify CRM.

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Connect Clientify MCP to LangChain

Create your Vinkius account to connect Clientify 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 Clientify with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain CRM actions in LangChain

Feed the output of `list_sales_deals` directly into your next chain link. Your agent evaluates current pipeline health before triggering follow-up logic. You avoid manual context switching by letting the model decide when to execute `get_deal_details` based on previous node results.

Trace your Clientify MCP Server

Connect your MCP server to LangSmith to monitor every latency spike and token count. You see exactly how your agent handles `list_crm_activities` in real time. Debugging becomes a matter of inspecting the trace logs. You verify if the agent correctly parsed the output from `list_clientify_contacts` without guessing.

Aggregate multiple data sources

Combine Clientify data with your existing vector stores inside one chain. The agent uses `list_sales_pipelines` to decide which documents to query next. This setup creates a unified flow where CRM records influence your search retrieval strategy. It keeps your logic centralized within your LangGraph implementation.

Setup guide

Set up Clientify 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 Clientify 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({
    "clientify-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 Clientify 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 Clientify. 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 Clientify MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. You then pass the tools directly into your agent constructor to finish the connection.

Yes. The tools are designed to work within stateful sessions, allowing the agent to reference previous contact lookups during the same execution loop.

It does. You can aggregate Clientify tools alongside other servers in a single MultiServerMCPClient instance.

Tool outputs return as structured JSON, which LangChain parses immediately. This allows your agent to pass data between steps without extra formatting.

Your CRM contact records never leave the secure Vinkius sandbox. LangChain only receives the specific data returned by the tool call you initiated.

Use it with your favorite AI tools

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