How to Use the Housecall Pro MCP in LlamaIndex

Query your field operations directly in LlamaIndex by indexing live jobs, estimates, and invoices into searchable vector data.

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Connect Housecall Pro MCP to LlamaIndex

Create your Vinkius account to connect Housecall Pro 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 Housecall Pro with LlamaIndex

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Build a queryable index of your service history

`list_jobs` grabs your historical work orders so LlamaIndex can index them into searchable vector stores. Your agent queries this database to find past solutions to tricky HVAC or plumbing issues instantly. Instead of digging through old tickets, you search using natural language. The index links job descriptions to the exact technicians who solved them.

Search Housecall Pro data with LlamaIndex RAG

`list_customers` feeds active client profiles directly into your retrieval-augmented generation pipelines. This lets your agent answer complex billing questions by grounding its answers in real customer files. You avoid hallucinations about past work because the model checks live data from `get_customer` first. Every response relies on actual records, not guesses.

Audit pricing structures with semantic search

`list_price_list_items` exposes your service catalog so LlamaIndex can map similar services and detect pricing inconsistencies. You can run semantic queries to find duplicate items or outdated rates across different regions. This setup highlights where your field team is undercharging. It matches raw estimates from `list_estimates` against the indexed price list to flag bad quotes.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Housecall Pro. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Housecall Pro MCP in LlamaIndex

Connect to the MCP server using BasicMCPClient, convert the tools to a tool list, and pass them to your agent. Your agent can then pull live data using list_jobs and index the text into a vector store.

Yes, LlamaIndex can run semantic search queries over the text retrieved from list_estimates. This lets you find matching quotes based on the description of the work rather than just ID numbers.

It uses RAG to ground the agent's answers in actual live data pulled from get_customer and get_job. The agent reads the real-time API response before writing a reply.

You can use this MCP server to fetch live technician schedules via list_technicians and store those records in any supported vector database. This lets you run semantic queries to find the right technician for a complex job.

Vinkius runs the server inside an isolated, zero-trust V8 sandbox. Your customer names and addresses fetched through get_customer are processed in memory and never cached on our servers.

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