How to Use the HyperTrack MCP in LlamaIndex

Q: How do I index HyperTrack data using LlamaIndex and MCP?

Use the MCP tool spec to pull data from tools like listtrips into your ingestion pipeline. LlamaIndex converts the JSON responses into document nodes for your vector store.

Q: Does LlamaIndex query live HyperTrack device statuses?

Yes, the getdevice tool allows your agent to fetch real-time telemetry on demand. The agent combines this live coordinate data with indexed historical data to answer complex logistics questions.

Q: Does this HyperTrack MCP Server support LlamaIndex sub-questions?

Yes, you set up a sub-question query engine that calls gettrip and getworker separately. The engine aggregates the physical-world inputs to resolve multi-part routing questions.

Index live HyperTrack coordinates and trip data directly into LlamaIndex vector stores using this MCP Server.

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Create your Vinkius account to connect HyperTrack 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 HyperTrack with LlamaIndex

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Index active worker locations into LlamaIndex

The `get_worker` tool fetches current profile and status details for any field agent. LlamaIndex indexes this live data, letting you query your workforce status using plain English instead of writing SQL. Running `list_workers` pulls your entire active roster into a searchable document format. This prevents your agent from hallucinating driver availability when planning new dispatches.

Query order histories with LlamaIndex RAG

The `get_order` tool retrieves specific payload details, destination coordinates, and completion statuses. Your pipeline indexes these records so users search past deliveries by customer sentiment or driver notes. You use `list_orders` to feed historical delivery logs into your vector database. This MCP Server keeps your index updated with fresh physical-world data.

Ground location queries with active geofences

The `list_geofences` tool outputs a complete list of your active monitoring zones. LlamaIndex turns these coordinates into searchable nodes, letting your agent instantly identify which driver is closest to a specific zone. When you call `get_geofence`, you get detailed entry and exit logs for that specific zone. This live context resolves user questions about delivery timing without manual database lookups.

Setup guide

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

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Common questions about HyperTrack MCP in LlamaIndex

Use the MCP tool spec to pull data from tools like `list_trips` into your ingestion pipeline. LlamaIndex converts the JSON responses into document nodes for your vector store.

Yes, the `get_device` tool allows your agent to fetch real-time telemetry on demand. The agent combines this live coordinate data with indexed historical data to answer complex logistics questions.

Yes, you set up a sub-question query engine that calls `get_trip` and `get_worker` separately. The engine aggregates the physical-world inputs to resolve multi-part routing questions.

You specify allowed tools in the tool spec configuration. This limits your agent to reading data with `list_orders` while blocking write operations if necessary.

Your device coordinates, geofence logs, and trip details pass directly from the secure MCP sandbox to your local vector store. No persistent telemetry data is saved on intermediate servers.

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