Cliengo 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 Cliengo 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 Cliengo. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Cliengo?"
)
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 Cliengo MCP Server
Connect your Cliengo account to any AI agent and take full control of your conversational marketing and lead management through natural conversation. Streamline how you capture and qualify leads via chatbot and WhatsApp natively.
LlamaIndex agents combine Cliengo 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 leads and contacts captured via Cliengo natively
- Conversation Intelligence — Access all chatbot or WhatsApp conversations and retrieve full message histories flawlessly
- Website Tracking — List all websites and projects where your Cliengo chatbot is installed securely
- Message Auditing — Retrieve all specific messages exchanged with a contact to understand their needs flawlessly
- User Management — List internal users and agents who manage conversations within your account securely
- Webhook Visibility — Monitor all configured webhooks for real-time lead data integration directly within your workspace
The Cliengo 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 Cliengo to LlamaIndex via MCP
Follow these steps to integrate the Cliengo 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 Cliengo
Why Use LlamaIndex with the Cliengo MCP Server
LlamaIndex provides unique advantages when paired with Cliengo through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cliengo tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cliengo tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cliengo, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cliengo tools were called, what data was returned, and how it influenced the final answer
Cliengo + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cliengo MCP Server delivers measurable value.
Hybrid search: combine Cliengo real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cliengo 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 Cliengo for fresh data
Analytical workflows: chain Cliengo queries with LlamaIndex's data connectors to build multi-source analytical reports
Cliengo MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Cliengo to LlamaIndex via MCP:
get_chat_history
Get the full message history for a specific conversation
get_contact_messages
Retrieve all messages exchanged with a specific contact
get_lead_details
Get detailed information for a specific contact
list_chat_conversations
List all chatbot or WhatsApp conversations
list_cliengo_leads
List all leads and contacts captured via Cliengo
list_cliengo_users
List all internal users and agents in the account
list_cliengo_webhooks
List all configured webhooks for real-time lead data
list_cliengo_websites
List all websites/projects where Cliengo is installed
Example Prompts for Cliengo in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Cliengo immediately.
"List all new leads from Cliengo today."
"Show me the chat history for Juan Perez."
"List all internal users who manage my Cliengo account."
Troubleshooting Cliengo MCP Server with LlamaIndex
Common issues when connecting Cliengo to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCliengo + LlamaIndex FAQ
Common questions about integrating Cliengo 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 Cliengo 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 Cliengo to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.