How to Use the UniOne MCP in LlamaIndex

Q: How do I use UniOne and LlamaIndex together in a pipeline?

You run the tool call first (e.g., checking getwebhook details), then pass that structured data to LlamaIndex for indexing, allowing future queries to retrieve that specific information.

Build RAG systems over UniOne API data with LlamaIndex.

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Connect UniOne MCP to LlamaIndex

Create your Vinkius account to connect UniOne 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 UniOne with LlamaIndex

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Indexing Email History using the MCP Server

When you run `send_email`, the result can be indexed into a vector store. This means your RAG app doesn't just answer questions; it grounds answers in actual, successful API data from past sends. Querying this history provides undeniable proof of what happened.

Discover Configurations with LlamaIndex

Forget digging through old documentation. Use `list_webhooks` to pull a list of all existing hooks into your knowledge base. You can then query, 'What was the webhook setup for Project Alpha?' and get a grounded answer instantly.

Auditing Templates with LlamaIndex

Templates are perfect for indexing. Instead of just reading the content, you index the metadata from `get_template`. This lets your knowledge agent search by status, owner, or last updated date against thousands of records.

Setup guide

Set up UniOne 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 UniOne 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 UniOne tools.",
)
response = await agent.run("List recent UniOne data")

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Common questions about UniOne MCP in LlamaIndex

LlamaIndex indexes the output of your MCP tool calls. When you use UniOne, successful actions like setting up a webhook or listing templates become searchable data points for retrieval-augmented generation.

You can query past API activity, such as which webhooks were configured (`set_webhook`) or what suppression lists currently exist. The answers are grounded in the live data of the MCP Server.

Yes. Because the MCP tools provide structured, verifiable outputs—like a list of available templates (`list_templates`)—they make excellent source documents for your index.

You run the tool call first (e.g., checking `get_webhook` details), then pass that structured data to LlamaIndex for indexing, allowing future queries to retrieve that specific information.

The server touches Email Templates. When you index this data, your AI client gets semantic understanding of template structure and content for retrieval purposes only.

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