How to Use the Close MCP in LangChain

Q: Can LangChain trace Close MCP server tool calls in LangSmith?

Yes, every call to listcloseleads or getopportunitydetails generates a trace in LangSmith. You can monitor latency, inspect the exact payloads, and debug your sales workflows in real-time.

Get your LangChain agents updating Close pipelines and pulling lead details directly within your running chains.

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Connect Close MCP to LangChain

Create your Vinkius account to connect Close 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 Close with LangChain

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Run Close lead research inside LangChain chains

The `list_close_leads` tool pulls lead records directly into your LangChain run context. Your agent uses this raw data to feed the next node in your custom graph, deciding autonomously whether to trigger a follow-up action or log a trace to LangSmith. By feeding `get_lead_details` output directly into your prompt templates, you'll bypass manual data entry entirely. LangChain handles the state transitions while the Close MCP server fetches the fresh sales data your model needs.

Map Close opportunities using this MCP Server

The `list_close_opportunities` tool exposes your entire sales pipeline to your LangChain agent. This lets your agent check current deal values and evaluate pipeline health during multi-step reasoning loops. You can chain this with `get_opportunity_details` to verify specific deal blockers. LangChain manages the tool-calling loop, passing the outputs downstream so your agent knows exactly which deal needs attention next.

Automate Close task queues with LangChain

The `list_close_tasks` tool lets your agent pull outstanding CRM reminders directly into your LangChain execution flow. Your agent inspects these tasks, pairs them with current lead statuses, and determines the next logical action. It uses `get_my_close_profile` to map tasks to the correct owner inside the chain. This keeps your sales reps on track so they don't have to manually dig through the Close UI.

Setup guide

Set up Close 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 Close 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({
    "close-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 Close 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 Close. 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 Close MCP in LangChain

You initialize the connection using the MultiServerMCPClient pointing to your Vinkius endpoint. From there, call client.get_tools() and pass them to your LangChain agent constructor to start querying leads.

Yes, every call to list_close_leads or get_opportunity_details generates a trace in LangSmith. You can monitor latency, inspect the exact payloads, and debug your sales workflows in real-time.

LangChain executes tool calls sequentially within your defined chain logic. If your agent calls list_close_leads repeatedly, you should configure standard LangChain retry behavior to handle API limits gracefully.

You can combine the Close tools with database or vector store tools in the same LangChain agent. The agent decides whether to query your database or fetch live CRM data.

Your Close API keys, lead details, and pipeline data never persist on Vinkius. The server runs in a secure sandbox, passing lead and opportunity data directly to your local LangChain execution environment.

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