How to Use the AI Receptionist MCP in LlamaIndex

Q: Can I filter AI Receptionist tools in LlamaIndex?

You can use the allowedtools filter during setup. This limits which functions your agent can execute, keeping your data access restricted.

Index your AI Receptionist data into LlamaIndex to build RAG apps that ground answers in actual customer conversation history.

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Connect AI Receptionist MCP to LlamaIndex

Create your Vinkius account to connect AI Receptionist 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 AI Receptionist with LlamaIndex

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Index receptionist knowledge in LlamaIndex

Feed the results of `list_aireceptionist_knowledge` into your LlamaIndex vector store. Your agent now references your actual business docs when answering callers. This turns static articles into queryable data. When a caller asks a question, your agent retrieves the relevant chunk from the index instead of guessing.

Search past interactions

Call `list_aireceptionist_conversations` and index the output. You can query your own history to find exactly what a specific customer asked last week. Your agent builds a semantic map of your business. It identifies patterns in customer behavior that you might miss by reading logs manually.

Ground bookings in data

Use `get_aireceptionist_booking` to verify appointment status before your agent proposes a new time slot. This prevents double-booking. The tool output is immediately searchable. Your agent checks the existing bookings in the index before finalizing any new request.

Setup guide

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

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Common questions about AI Receptionist MCP in LlamaIndex

It converts the tool responses into vector embeddings. This allows your agent to perform semantic searches over your entire call history.

You can use the allowed_tools filter during setup. This limits which functions your agent can execute, keeping your data access restricted.

Your customer phone numbers and transcripts stay within the Vinkius sandbox. Only the processed embeddings are stored in your index.

It integrates directly via the McpToolSpec. You pass the tools to your agent, which then uses them to pull fresh data for your index.

You trigger the list tools periodically to refresh your index. This ensures your agent always knows about the latest appointments and knowledge base updates.

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