How to Use the MRPeasy MCP in LlamaIndex

Q: How do I get started with LlamaIndex and MRPeasy?

Install the llama-index-tools-mcp package. Create a BasicMCPClient with your Vinkius server URL, then wrap it in the McpToolSpec. The spec will expose all the MRPeasy tools for your LlamaIndex agent to use.

Q: Can I choose which MRPeasy tools my LlamaIndex agent can access?

Yes. The McpToolSpec allows you to pass an allowedtools list. This lets you restrict the agent to only use specific functions, like read-only tools, for certain tasks, giving you tight control over what the agent can do.

Q: What's a good use case for LlamaIndex with MRPeasy data?

Build a daily operations summary. Have an agent run listinvoices, listmanufacturingorders, and liststockitems every morning. Index the results, then ask it to generate a report on 'yesterday's shipments, current production blockers, and low-stock warnings'.

Give LlamaIndex a real-time, queryable memory of your factory floor using live MRPeasy data.

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Connect MRPeasy MCP to LlamaIndex

Create your Vinkius account to connect MRPeasy 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 MRPeasy with LlamaIndex

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Index Your Live Production Data

Turn your MRPeasy data into a knowledge base your agent can actually understand. With LlamaIndex, you can set up a process to periodically call tools like `list_manufacturing_orders` and `list_stock_items` and feed the results directly into a vector index. Now, when you ask your agent a question—like "which orders are blocked waiting for part #5542?"—it isn't just guessing. It's performing a semantic search over a constantly updated index of your real-world operations. You get answers grounded in fact, not just LLM predictions.

Build a Smarter RAG Pipeline

Combine static documents with live API data. Your agent can retrieve a work instruction manual from a document and then use the `get_stock_item` tool to check if the required parts are actually in stock right now. It gets this live context from the MRPeasy MCP Server. This creates a powerful RAG (Retrieval-Augmented Generation) system where the 'retrieval' part includes real-time operational data. Your agent can answer questions with context that's impossible to get from documents alone, like checking workstation availability with `list_work_stations`.

Query Your Supply Chain in Plain English

Let your team ask complex questions without writing a single line of code. Once you've indexed your data from tools like `list_customer_orders`, `list_purchase_orders`, and `list_vendors`, anyone can query the system naturally. Ask things like, "Show me all purchase orders from 'Supplier X' that are late and the customer orders they are affecting." LlamaIndex translates the query, finds the relevant data from its index of MRPeasy tool calls, and synthesizes a direct answer. It’s a huge step up from exporting CSVs.

Setup guide

Set up MRPeasy 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 MRPeasy 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 MRPeasy tools.",
)
response = await agent.run("List recent MRPeasy 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 MRPeasy. 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 MRPeasy MCP in LlamaIndex

Install the `llama-index-tools-mcp` package. Create a `BasicMCPClient` with your Vinkius server URL, then wrap it in the `McpToolSpec`. The spec will expose all the MRPeasy tools for your LlamaIndex agent to use.

Yes. The `McpToolSpec` allows you to pass an `allowed_tools` list. This lets you restrict the agent to only use specific functions, like read-only tools, for certain tasks, giving you tight control over what the agent can do.

Build a daily operations summary. Have an agent run `list_invoices`, `list_manufacturing_orders`, and `list_stock_items` every morning. Index the results, then ask it to generate a report on 'yesterday's shipments, current production blockers, and low-stock warnings'.

The data is as fresh as your last tool call. You control how often the agent calls the MRPeasy MCP Server to update its index. For critical data, you can run the indexing process every few minutes.

This MCP Server handles sensitive manufacturing data, including invoices, purchase orders, and vendor lists. Vinkius fetches all data over encrypted connections and isolates each server instance, ensuring your operations data isn't exposed.

Use it with your favorite AI tools

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