How to Use the Clientify MCP in LangChain

Q: Can LangChain update existing contacts in Clientify?

Yes, the updatecontact tool handles this directly. Your chain can pull contact details, analyze them, and write updates back to the CRM.

Q: Can I use multiple Clientify pipelines in one LangChain run?

The listpipelines tool lets your agent query all active pipelines. From there, the chain routes the deal to the correct pipeline based on your custom logic.

Feed real-time Clientify CRM data directly into your LangChain reasoning loops to close deals faster.

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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

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Claude

Perplexity

Automate lead triage in LangChain chains

The `list_contacts` tool pulls your latest Clientify marketing leads straight into your active reasoning chain. Your LangChain agent checks these records to see who needs a follow-up, bypassing manual CSV exports entirely. LangSmith traces every step of this lookup, showing you the raw payload and token cost. You get a clear view of how your agent filters contacts before passing them to the next step.

Sync deals across pipelines using this MCP Server

The `create_deal` tool lets your agent build new sales opportunities in Clientify based on conversation context. When a prospect shows interest in your LangChain chain, the agent spins up a deal automatically. You can chain this with `list_pipelines` to ensure the deal lands in the correct sales stage. The agent evaluates the prospect's budget, selects the right pipeline, and writes the record.

Track agent tasks in real time

The `create_activity` tool writes follow-ups and tasks directly to your Clientify calendar. Your LangChain agent schedules these tasks immediately after updating a prospect's record. Using the MultiServerMCPClient setup, you can combine this with your calendar tools. The agent matches your availability and logs the task in Clientify without dropping the ball.

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-alternative-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 langchain-mcp-adapters package and initialize the MultiServerMCPClient with your Vinkius endpoint. This exposes all ten Clientify tools to your agent in a single call.

Yes, the `update_contact` tool handles this directly. Your chain can pull contact details, analyze them, and write updates back to the CRM.

Your ReAct agent catches errors using standard LangChain runnables. If `list_activities` times out, the agent logs the failure in LangSmith and retries.

The `list_pipelines` tool lets your agent query all active pipelines. From there, the chain routes the deal to the correct pipeline based on your custom logic.

Your contact records, deals, and activities never touch external training sets. Vinkius runs this MCP Server inside a zero-trust, ephemeral V8 isolate that wipes your CRM data from memory the moment the execution ends.

Use it with your favorite AI tools

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