Close MCP Server for LangChainGive LangChain instant access to 6 tools to Create Lead, Get Current User, Get Lead Details, and more
LangChain is the leading Python framework for composable LLM applications. Connect Close through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The Close app connector for LangChain is a standout in the Communication Messaging category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"close-alternative": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Close, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Close MCP Server
Connect your Close CRM account to any AI agent and take full control of your sales workflows through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Close through native MCP adapters. Connect 6 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Lead Management — List all leads in your pipeline and retrieve full company profiles with contacts, custom fields, and activity history
- Lead Creation — Add new leads directly from conversation, including company name and website URL
- Opportunity Tracking — Monitor your active deals with stage, value, and expected close dates
- Task Management — Review pending and completed CRM tasks to stay on top of follow-ups
- User Context — Retrieve your authenticated user profile to understand your current permissions and role
The Close MCP Server exposes 6 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 Close tools available for LangChain
When LangChain connects to Close through Vinkius, your AI agent gets direct access to every tool listed below — spanning lead-management, pipeline-tracking, inside-sales, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new lead
Get information about the authenticated API user
Get details of a specific lead
List CRM tasks
List all leads in Close CRM
List sales opportunities
Connect Close to LangChain via MCP
Follow these steps to wire Close into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Close MCP Server
LangChain provides unique advantages when paired with Close through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Close MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Close queries for multi-turn workflows
Close + LangChain Use Cases
Practical scenarios where LangChain combined with the Close MCP Server delivers measurable value.
RAG with live data: combine Close tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Close, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Close tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Close tool call, measure latency, and optimize your agent's performance
Example Prompts for Close in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Close immediately.
"List all my leads in Close and highlight the ones added this week."
"Create a new lead for the company 'Nordic AI Labs' with their website nordicailabs.com."
"Show my active sales opportunities and any overdue tasks I need to handle."
Troubleshooting Close MCP Server with LangChain
Common issues when connecting Close to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersClose + LangChain FAQ
Common questions about integrating Close MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.