Trengo MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Ticket, Create Webhook, Get Account Profile, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Trengo as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Trengo app connector for LlamaIndex is a standout in the Communication Messaging category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Trengo. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Trengo?"
)
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 Trengo MCP Server
Connect your Trengo omnichannel inbox to any AI agent and simplify how you manage customer conversations, team collaboration, and support tickets through natural conversation.
LlamaIndex agents combine Trengo tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Unified Inbox Management — List all tickets and conversations across WhatsApp, Email, and Chat in one place.
- Ticket Control — Create new support tickets, update statuses (OPEN, CLOSED, ASSIGNED), and manage assignments via AI.
- Omichannel Messaging — Send messages to customers or add internal team notes to any conversation.
- Contact & Channel Directory — List your customer database and verify all configured communication channels.
- Team Coordination — Query team member lists to understand availability and workload.
- Event Monitoring — List and create webhooks to track conversation events in real-time.
The Trengo MCP Server exposes 12 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.
All 12 Trengo tools available for LlamaIndex
When LlamaIndex connects to Trengo through Vinkius, your AI agent gets direct access to every tool listed below — spanning omnichannel-inbox, helpdesk-ticketing, shared-inbox, 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 ticket
Create a new webhook
Get current user profile
Get ticket details
). List communication channels
List all contacts
List ticket messages
List team users
List all support tickets
List configured webhooks
Send a message
Update ticket status
Connect Trengo to LlamaIndex via MCP
Follow these steps to wire Trengo into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Trengo MCP Server
LlamaIndex provides unique advantages when paired with Trengo through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Trengo tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Trengo tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Trengo, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Trengo tools were called, what data was returned, and how it influenced the final answer
Trengo + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Trengo MCP Server delivers measurable value.
Hybrid search: combine Trengo real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Trengo 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 Trengo for fresh data
Analytical workflows: chain Trengo queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Trengo in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Trengo immediately.
"List all currently open support tickets."
"Show me the last 3 messages for ticket #88231."
"Close ticket #10293 as 'CLOSED' and add a note 'Resolved via AI'."
Troubleshooting Trengo MCP Server with LlamaIndex
Common issues when connecting Trengo to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTrengo + LlamaIndex FAQ
Common questions about integrating Trengo MCP Server with LlamaIndex.