How to Use the HCSS MCP in LlamaIndex

Q: How do I set up HCSS with LlamaIndex?

Install llama-index-tools-mcp via pip. Initialize BasicMCPClient with the Vinkius endpoint, then pass it to McpToolSpec to expose the tools to your FunctionAgent.

Q: Can LlamaIndex index HCSS cost codes?

Yes. You call listcostcodes and embed the results into your vector store. Your RAG app can then answer field questions about budget allocations.

Q: How does LlamaIndex handle HCSS equipment tracking?

The agent queries getequipmentlocation and getequipmentmeters. It indexes these snapshots so you can ask semantic questions about fleet deployment over time.

Index your HCSS construction data into searchable vector stores using LlamaIndex.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect HCSS MCP to LlamaIndex

Create your Vinkius account to connect HCSS 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.

GDPR Free for Subscribers

Setup HCSS with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build a RAG pipeline for estimates

Query `list_estimates` and `list_precon_projects` to pull your upcoming pipeline into a LlamaIndex vector store. Your application indexes the HeavyBid data so project managers can query historical pricing models. Instead of digging through old folders, you just ask the index. The system retrieves specific records using `get_bid_items` and grounds its answers in actual past performance rather than guessing.

Ground LlamaIndex in active job data

Connect LlamaIndex to `list_jobs` and `list_cost_codes` to build a semantic search engine for your active construction sites. Field engineers can query the exact cost code structure for any specific phase of work. Construction projects generate massive amounts of unstructured communication. By indexing the structured ERP data alongside your documents, your RAG application knows exactly which business units own which tasks via `list_business_units`.

Query fleet utilization history

Fetch asset data using `list_equipment` and `get_equipment_meters` to populate your knowledge base with hardware status. You turn raw telematics into a searchable history of machine wear and tear. Planners need to know what machines are available. When they ask the index for an excavator, the system checks the latest `get_equipment_location` coordinates and returns the closest available asset.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HCSS. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect HCSS now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about HCSS MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Initialize `BasicMCPClient` with the Vinkius endpoint, then pass it to `McpToolSpec` to expose the tools to your FunctionAgent.

Yes. You call `list_cost_codes` and embed the results into your vector store. Your RAG app can then answer field questions about budget allocations.

Yes. You can restrict the agent to just read-only endpoints like `list_business_units` if you want to limit the scope of the index.

The agent queries `get_equipment_location` and `get_equipment_meters`. It indexes these snapshots so you can ask semantic questions about fleet deployment over time.

The system routes all requests through a zero-trust architecture. When LlamaIndex fetches `get_equipment_location` or `list_cost_codes`, the MCP Server processes the request in memory and immediately drops the connection state. Nothing persists on our end.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript