How to Use the McLeod Software MCP in LlamaIndex

Q: How do I search McLeod Software records using LlamaIndex?

You index the outputs of listorders and searchorders. Once indexed, you query your knowledge base to find specific shipment details instantly.

Index your McLeod Software logistics data into LlamaIndex for grounded, searchable carrier and order intelligence.

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Connect McLeod Software MCP to LlamaIndex

Create your Vinkius account to connect McLeod Software 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 McLeod Software with LlamaIndex

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Index McLeod Software data with LlamaIndex

Turn your transportation records into a searchable knowledge base. By running `list_customers` and `list_orders` through your indexing pipeline, you make historical data queryable. Your RAG application references this index to answer complex questions. Instead of guessing, the agent retrieves grounded facts from your actual system records.

Contextual search in LlamaIndex

Query your entire fleet history using semantic search. When you call `list_movements` and `list_stops`, the output is ingested directly into your vector store. This gives your agent a long-term memory of past shipments. It compares new load requests against previous movement patterns stored in your index.

Automated record retrieval for LlamaIndex

Keep your LlamaIndex knowledge current with live data calls. Your agent fetches the latest `list_carriers` or `list_drivers` entries whenever the index requires a refresh. This keeps your responses accurate. You avoid stale information by ensuring the agent pulls fresh API data before executing any search or summary task.

Setup guide

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

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

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Common questions about McLeod Software MCP in LlamaIndex

You index the outputs of `list_orders` and `search_orders`. Once indexed, you query your knowledge base to find specific shipment details instantly.

Absolutely. You populate your vector store with historical movement data from `list_movements`, enabling the agent to analyze trends over long timeframes.

You define the trigger. Once your script calls the tool, the returned data is formatted and pushed into your index for semantic retrieval.

It treats the JSON output as a document. The text is embedded and stored, allowing for deep semantic understanding of your dispatch operations.

The data stays within your controlled vector store. We use encrypted transport for all API calls, and the information is never shared with third-party model providers.

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