How to Use the Axle MCP in LlamaIndex

Q: Can LlamaIndex index the output of the Axle document tools?

Yes, the output from listdocuments can be ingested directly into your document store. This allows your agent to perform semantic searches over shipping paperwork and associate them with specific loads.

Q: How do I limit which Axle tools LlamaIndex can access?

You can use the allowedtools filter when setting up the McpToolSpec. This lets you restrict the agent to read-only tools like listvehicles while blocking write tools like updateload.

Q: What is the best way to handle fleet updates in the index?

Set up a recurring task that calls getvehiclelocation and updates your vector store. This keeps your search index fresh without lagging behind the actual physical trucks.

Index your fleet logistics into LlamaIndex for semantic search using this MCP Server.

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Connect Axle MCP to LlamaIndex

Create your Vinkius account to connect Axle 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 Axle with LlamaIndex

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Index live fleet data into your LlamaIndex RAG pipeline

Feeding your LlamaIndex vector stores is easy once `list_loads` fetches active shipment data. The agent indexes these active shipment details into a vector store for fast, semantic search. When a customer asks about their shipment status, the agent queries the index instead of hitting the live database again. It combines the indexed data with real-time GPS coordinates from `get_vehicle_location` to give an exact, grounded answer.

Build searchable logs of driver hours and documents

To build a searchable compliance index in LlamaIndex, `list_documents` retrieves scanned shipping paperwork. The agent runs this alongside `get_driver_availability` to build an index of driver availability and scanned paperwork. If a dispatcher needs to find which drivers have remaining hours and the right paperwork for a hazardous load, they can search the index. LlamaIndex uses these indexed tool outputs to answer complex compliance questions without manual filtering.

Monitor vehicle status with semantic querying

Pulling a complete inventory of fleet assets via `list_vehicles` enables deep semantic querying within LlamaIndex. The agent can monitor vehicle health and profile details retrieved from `get_vehicle` to flag maintenance patterns. This setup avoids constant API polling. You query the local vector index for general fleet questions, and the agent only calls the live tools when it needs to update a specific record.

Setup guide

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

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Common questions about Axle MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the client with your Vinkius URL. Then, convert the tools using McpToolSpec and pass them directly to your LlamaIndex FunctionAgent.

Yes, the output from list_documents can be ingested directly into your document store. This allows your agent to perform semantic searches over shipping paperwork and associate them with specific loads.

You can use the allowed_tools filter when setting up the McpToolSpec. This lets you restrict the agent to read-only tools like list_vehicles while blocking write tools like update_load.

Set up a recurring task that calls get_vehicle_location and updates your vector store. This keeps your search index fresh without lagging behind the actual physical trucks.

All communication goes through a secure V8 sandbox on Vinkius. Your sensitive driver profiles from get_driver and load details are processed in memory and never written to persistent disk on our servers.

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