Clientjoy MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Clientjoy 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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({
"clientjoy": {
"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 Clientjoy, 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 Clientjoy MCP Server
Connect your Clientjoy account to any AI agent and take full control of your agency operations through natural conversation. Streamline how you manage the entire lifecycle from lead capture to final invoicing natively.
LangChain's ecosystem of 500+ components combines seamlessly with Clientjoy through native MCP adapters. Connect 8 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 Oversight — List and retrieve details for all sales leads and their capture status natively
- Contact Intelligence — Access and monitor all client contacts and relationship history flawlessly
- Invoicing Logistics — List all agency invoices and monitor their payment status flawlessly
- Project Management — Access and monitor all client projects and their constituent tasks securely
- Sales Pipelines — List and review quotes and proposals sent to potential clients flawlessly
- Profile Visibility — Access your own user profile and core workspace metadata directly within your workspace flawlessly
The Clientjoy MCP Server exposes 8 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.
How to Connect Clientjoy to LangChain via MCP
Follow these steps to integrate the Clientjoy MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from Clientjoy via MCP
Why Use LangChain with the Clientjoy MCP Server
LangChain provides unique advantages when paired with Clientjoy through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Clientjoy 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 Clientjoy queries for multi-turn workflows
Clientjoy + LangChain Use Cases
Practical scenarios where LangChain combined with the Clientjoy MCP Server delivers measurable value.
RAG with live data: combine Clientjoy tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Clientjoy, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Clientjoy tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Clientjoy tool call, measure latency, and optimize your agent's performance
Clientjoy MCP Tools for LangChain (8)
These 8 tools become available when you connect Clientjoy to LangChain via MCP:
get_contact_crm_details
Get detailed information for a specific contact
get_lead_crm_details
Get detailed information for a specific lead
get_my_clientjoy_profile
Retrieve information about the authenticated workspace user
list_agency_invoices
List all invoices and their payment status
list_agency_projects
List all client projects tracked in Clientjoy
list_clientjoy_contacts
List all contacts and clients stored in the CRM
list_clientjoy_leads
List all sales leads captured in Clientjoy
list_sales_quotes
List sales quotes and proposals sent to clients
Example Prompts for Clientjoy in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Clientjoy immediately.
"List all my new leads in Clientjoy."
"Show me my unpaid invoices."
"What is the status of the 'Website Redesign' project?"
Troubleshooting Clientjoy MCP Server with LangChain
Common issues when connecting Clientjoy to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersClientjoy + LangChain FAQ
Common questions about integrating Clientjoy 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.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Clientjoy with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Clientjoy to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.