TurfHop MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Customer, Create Job, Get Customer, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add TurfHop as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The TurfHop app connector for LlamaIndex is a standout in the Erp Operations category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to TurfHop. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in TurfHop?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About TurfHop MCP Server
Connect your TurfHop account to any AI agent and simplify how you coordinate your field service operations, scheduling, and billing through natural conversation.
LlamaIndex agents combine TurfHop tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Customer Management — List and search customer records, create new profiles, and retrieve complete service histories.
- Job Scheduling — List all service jobs, create new assignments, and update job details or statuses programmatically.
- Billing & Invoicing — Monitor your cash flow by listing invoices, quotes, and payment statuses for your services.
- Service Catalog — Browse your offered products and services to identify pricing and availability.
- Operational tracking — Fetch detailed metadata for specific jobs or customers to stay on top of your mobile workforce.
The TurfHop MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 TurfHop tools available for LlamaIndex
When LlamaIndex connects to TurfHop through Vinkius, your AI agent gets direct access to every tool listed below — spanning lawn-care, scheduling, route-planning, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Pass customer data as a JSON string. Create a new customer
Pass job data as a JSON string. Create a new service job
Get customer details by ID
Get invoice details
Get job details
List all customers
List all invoices
List all service jobs
List all products and services
List all quotes
Update an existing customer
Update an existing job
Connect TurfHop to LlamaIndex via MCP
Follow these steps to wire TurfHop into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the TurfHop MCP Server
LlamaIndex provides unique advantages when paired with TurfHop through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine TurfHop tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain TurfHop tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query TurfHop, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what TurfHop tools were called, what data was returned, and how it influenced the final answer
TurfHop + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the TurfHop MCP Server delivers measurable value.
Hybrid search: combine TurfHop real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query TurfHop to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying TurfHop for fresh data
Analytical workflows: chain TurfHop queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for TurfHop in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with TurfHop immediately.
"List all active service jobs in my TurfHop account."
"Search for a customer named 'Michael Scott'."
"Show me all unpaid invoices from this month."
Troubleshooting TurfHop MCP Server with LlamaIndex
Common issues when connecting TurfHop to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTurfHop + LlamaIndex FAQ
Common questions about integrating TurfHop MCP Server with LlamaIndex.