How to Use the Picktime MCP in LlamaIndex

Q: Can I use Picktime with LlamaIndex to find booking history?

Yes. The agent calls listbookings to retrieve the data. You then store this in your vector index to enable complex semantic queries over past appointments.

Q: Does the Picktime MCP Server support LlamaIndex RAG apps?

It does. By using McpToolSpec, you incorporate these tools into your agent. This allows the system to pull live data into your query engine.

Ground LlamaIndex queries in real-time booking data. Use the Picktime MCP Server to index your schedule into a searchable knowledge base.

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Connect Picktime MCP to LlamaIndex

Create your Vinkius account to connect Picktime to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Picktime with LlamaIndex

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Index Picktime data in LlamaIndex

Convert `list_bookings` output into a vector store for semantic search. Your RAG application retrieves past booking trends instantly. This makes your knowledge base context-aware. You can query the agent about historical booking patterns without hitting the API repeatedly.

Query schedules with LlamaIndex

Let your agent use `list_services` and `get_service_details` to answer customer questions accurately. It grounds the response in your current service configuration. This prevents the agent from making up pricing or duration info. Everything is backed by your actual service data.

Use MCP Server for live data

Combine static documents with live `list_classes` data inside your LlamaIndex pipeline. The agent fetches current class rosters to provide up-to-date information. This approach merges your internal docs with live scheduling facts. It ensures the agent knows exactly what is happening in your business right now.

Setup guide

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

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Common questions about Picktime MCP in LlamaIndex

The MCP client exposes the tools to your agent, which then fetches the data. You can index these results to make them searchable within your LlamaIndex workflow.

Yes. The agent calls `list_bookings` to retrieve the data. You then store this in your vector index to enable complex semantic queries over past appointments.

It does. By using `McpToolSpec`, you incorporate these tools into your agent. This allows the system to pull live data into your query engine.

It is essential for scoping your data. By resolving the location first, you ensure your LlamaIndex knowledge base contains only relevant branch information.

The server processes booking records and contact details securely via the endpoint token. Data is only moved into your index when the agent triggers a tool call, keeping access strictly controlled.

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