How to Use the TurfHop MCP in LlamaIndex

Q: How does LlamaIndex use TurfHop to index information?

When your agent calls a tool—like createcustomer or getjob—LlamaIndex captures the resulting data. It then converts that structured JSON into semantic chunks for vector storage.

Q: Can LlamaIndex help me understand billing trends with TurfHop?

Absolutely. By indexing multiple results from listinvoices, you can query across time periods and get answers grounded in the actual invoice data, making trend analysis easier.

Q: If I update a product using TurfHop, how does LlamaIndex know it?

You must run the listproducts tool after updating. This ensures the latest catalog information is passed to your client and subsequently indexed into your knowledge base.

Ground your AI queries in facts using LlamaIndex with TurfHop.

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Connect TurfHop MCP to LlamaIndex

Create your Vinkius account to connect TurfHop 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 TurfHop with LlamaIndex

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Query past job and customer records

LlamaIndex doesn't just run a tool; it indexes the results for later search. You can query past sessions to ask, 'What was the scope of work on Job X?' using `get_job`. The output gets indexed as searchable knowledge. This means you get answers grounded in actual job data, not something hallucinated by your agent.

Build RAG over billing history

Need to know the typical pricing structure? You can index all results from `list_invoices` and `get_invoice`. Your application then treats this historical data like a searchable document. This lets you query, for example, 'What was billed last quarter?' and get an answer derived directly from the API call.

Semantic search of customer details

Instead of just searching by ID, you can ask complex questions about your client base. By indexing data retrieved via `list_customers` or `get_customer`, you build a knowledge graph. Your RAG application combines this live API data with internal documents for one unified search.

Setup guide

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

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Common questions about TurfHop MCP in LlamaIndex

When your agent calls a tool—like `create_customer` or `get_job`—LlamaIndex captures the resulting data. It then converts that structured JSON into semantic chunks for vector storage.

Absolutely. By indexing multiple results from `list_invoices`, you can query across time periods and get answers grounded in the actual invoice data, making trend analysis easier.

The server touches customer records (customer details), service job records (job details), and financial billing information (invoice details). All of these can be indexed.

You must run the `list_products` tool after updating. This ensures the latest catalog information is passed to your client and subsequently indexed into your knowledge base.

You query the system, which searches the indexed results from `list_quotes`. It returns an answer grounded in that specific quote ID and its associated details.

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