How to Use the Vapi MCP in LlamaIndex

Q: How can LlamaIndex use Vapi data for semantic search?

You run getcalldetails and feed the resulting transcripts and metrics into your vector store. Your AI client then queries this index, retrieving context specific to past calls.

Q: When using Vapi and LlamaIndex together, how do I ensure data accuracy?

By indexing live API data—like those provided by getcalldetails—you eliminate hallucinations. The answers are always grounded in the actual call records.

Ground your agents in real data using Vapi and LlamaIndex.

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Connect Vapi MCP to LlamaIndex

Create your Vinkius account to connect Vapi 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 Vapi with LlamaIndex

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Index Call Transcripts for RAG

When you call `get_call_details`, the tool returns rich transcripts, metrics, and full records. LlamaIndex can take this structured data and index it into a vector store. This means your AI client can query past sessions or configurations using semantic search, getting answers grounded in actual API data instead of guessing.

Define and Update Personas

Start by setting up the core intelligence with `create_assistant`, defining the model, transcriber, and voice. This initial configuration becomes searchable knowledge. Later on, if you need to refine the agent's behavior, `update_assistant` allows you to modify that persona, keeping your knowledge base current.

Discover Available Tools

To build a complete index, your client needs to know what data sources exist. You use `list_agent_tools` to see every tool Vapi exposes. It's also key to check which phone numbers are active by running `list_phone_numbers`, ensuring the knowledge you index is always pointing to valid resources.

Setup guide

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

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Common questions about Vapi MCP in LlamaIndex

You run `get_call_details` and feed the resulting transcripts and metrics into your vector store. Your AI client then queries this index, retrieving context specific to past calls.

Yes. Because the MCP Server output is structured data (like call details), it indexes perfectly into a knowledge base that LlamaIndex can query using its core framework.

It provides a list of recent calls. This is the starting point for building an index, giving your agent the titles or identifiers needed to retrieve detailed call transcripts later.

By indexing live API data—like those provided by `get_call_details`—you eliminate hallucinations. The answers are always grounded in the actual call records.

The server handles raw transcripts and call metrics. These are the identifiable conversational records that you must manage within your index.

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