How to Use the Trengo MCP in LlamaIndex

Q: Can I find specific team information using Trengo and LlamaIndex?

Absolutely. You can run listteammembers to get the user list and index it. Your application can then answer questions like, 'Who is assigned to ticket XYZ?' by querying the indexed data.

Q: Does Trengo support live webhook data access with LlamaIndex?

Yes. You use listwebhooks to gather configuration details, and these records are perfect for indexing. This lets your application ground answers about setup processes using real API data.

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Connect Trengo MCP to LlamaIndex

Create your Vinkius account to connect Trengo to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Trengo with LlamaIndex

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Indexing MCP Server Data

LlamaIndex takes the outputs from the MCP Server and turns them into searchable knowledge. You can query past support tickets by calling `list_tickets`, and those results get indexed. When a user asks about an old issue, your agent retrieves the answer from this specialized Trengo knowledge base instead of hallucinating.

RAG for Contact Management

Want to know all the communication history for a client? Run `list_messages` and pass those results into LlamaIndex. You'll get an index that allows you to ask natural language questions like, 'What did we promise this customer last month?' and it pulls the answer from actual stored conversation data.

Retrieving Account Configurations

The server lets you manage webhooks. Use `list_webhooks` to pull a list of active integrations, then index that list. Later, your agent can search the knowledge base and tell you exactly which external services are already connected without needing to call any tools directly.

Setup guide

Set up Trengo MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Trengo MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Trengo tools.",
)
response = await agent.run("List recent Trengo data")

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Common questions about Trengo MCP in LlamaIndex

LlamaIndex integrates by treating the MCP Server as a data source. You pass the tool specifications, and your agent can use those tools to gather raw data which is then fed into the vector store for semantic search.

Trengo deals with customer conversations across multiple channels. When indexed, this includes ticket details (`get_ticket`), message logs, and contact profiles, making all communication searchable.

Absolutely. You can run `list_team_members` to get the user list and index it. Your application can then answer questions like, 'Who is assigned to ticket XYZ?' by querying the indexed data.

Yes. You use `list_webhooks` to gather configuration details, and these records are perfect for indexing. This lets your application ground answers about setup processes using real API data.

The server touches customer conversation data. When you index this, make sure the retrieval mechanism only exposes necessary fields from tools like `get_ticket` to maintain strict data boundaries.

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