Clientjoy MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Clientjoy as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Clientjoy. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Clientjoy?"
)
print(response)
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.
LlamaIndex agents combine Clientjoy tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Clientjoy MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Clientjoy
Why Use LlamaIndex with the Clientjoy MCP Server
LlamaIndex provides unique advantages when paired with Clientjoy through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Clientjoy tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Clientjoy tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Clientjoy, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Clientjoy tools were called, what data was returned, and how it influenced the final answer
Clientjoy + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Clientjoy MCP Server delivers measurable value.
Hybrid search: combine Clientjoy real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Clientjoy to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Clientjoy for fresh data
Analytical workflows: chain Clientjoy queries with LlamaIndex's data connectors to build multi-source analytical reports
Clientjoy MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Clientjoy to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Clientjoy to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpClientjoy + LlamaIndex FAQ
Common questions about integrating Clientjoy MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.