How to Use the Lamha MCP in LlamaIndex

Q: How do I build a RAG agent with Lamha and LlamaIndex?

You'll use the McpToolSpec to wrap the Lamha client, which converts the tools for LlamaIndex. Then you pass these tools to a FunctionAgent. The agent will automatically use the tools to fetch data and answer questions.

Turn Lamha logistics data into a searchable knowledge base with LlamaIndex. Build RAG agents that remember inventory and orders.

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Connect Lamha MCP to LlamaIndex

Create your Vinkius account to connect Lamha 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 Lamha with LlamaIndex

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Index Your Live Logistics Data

Your agent calls `list_inventory` or `list_warehouses` once. LlamaIndex then ingests and indexes that data into a vector store. Now, your agent has a memory of your stock levels and locations. When a user asks, "Do we have any large blue shirts in the Riyadh warehouse?" in Arabic, the agent queries its indexed knowledge. It doesn't need to call the API again, giving you faster answers that are grounded in real, previously fetched data.

Query Past Orders with LlamaIndex

Build a knowledge base from your order history. As your agent uses `list_orders` and `get_order`, LlamaIndex can be configured to index the results. This turns your operational data into a queryable asset. Your team can ask semantic questions like, "What were our top 3 most returned items last month?" or "Show me all orders from Kuwait City that used Aramex." The agent finds answers from its memory, not by running complex database reports.

Ground Responses in Factual Carrier Data

Stop your agent from making things up. When you use the Lamha MCP Server, your agent can ground its answers in facts by calling `list_carriers` and `check_city_coverage`. LlamaIndex makes this even better by indexing that information. If a user asks if you ship to a specific neighborhood, the agent's answer is based on the indexed results of `check_city_coverage`. This means your chatbot gives accurate shipping information every time, based on data from your actual logistics provider.

Setup guide

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

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Common questions about Lamha MCP in LlamaIndex

You control the refresh interval. You can set up your LlamaIndex application to periodically call `list_inventory` and re-index the results, ensuring your agent's knowledge base doesn't get stale.

Yes, that's what it's designed for. LlamaIndex can combine your internal documents (like return policies) with the live data from Lamha's tools into a single, unified index for your agent to query.

You'll use the `McpToolSpec` to wrap the Lamha client, which converts the tools for LlamaIndex. Then you pass these tools to a FunctionAgent. The agent will automatically use the tools to fetch data and answer questions.

Not necessarily. LlamaIndex first checks if it can answer the question from its indexed data. If the answer is there, it uses the cached information. If not, it calls the appropriate Lamha tool, like `get_order`, to fetch live data.

The security of your indexed data depends on the vector database you choose. The MCP server itself processes requests statelessly. When LlamaIndex stores the output of tools like `list_orders`, it's your responsibility to secure the vector index where that order data resides.

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