How to Use the Katana MCP in LlamaIndex

Q: Can I use Katana and LlamaIndex for historical analysis?

You index past orders from listsalesorders to build a repository of historical trends. The agent then performs semantic searches on this data to identify patterns.

Q: Does the Katana MCP Server support filtering in LlamaIndex?

You use the allowedtools filter to restrict which endpoints the agent accesses. This gives you control over what the LlamaIndex agent can see and modify.

Index your manufacturing data into searchable knowledge bases using Katana and LlamaIndex.

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Connect Katana MCP to LlamaIndex

Create your Vinkius account to connect Katana 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 Katana with LlamaIndex

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LlamaIndex semantic inventory search

Retrieve `list_inventory` data and store it directly in your vector index. You search across your entire stock status using natural language queries. Your agent grounds its answers in the live results returned by `get_inventory_by_variant`. This stops the agent from guessing inventory counts.

Unified manufacturing knowledge with LlamaIndex

Merge your `list_manufacturing_orders` and `list_materials` data into a single queryable index. It makes your production history instantly accessible to the agent. This approach turns static API responses into a searchable knowledge base. You ask questions about material lead times and get back specific, data-backed answers.

Build RAG pipelines for Katana MCP Server

Use the MCP tool spec to turn API calls into context-aware functions for your RAG pipeline. It forces the agent to consult live manufacturing data before responding. Every query passes through the tool list for validation. This ensures your AI client provides accurate information about current purchase orders.

Setup guide

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

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Common questions about Katana MCP in LlamaIndex

Use the BasicMCPClient to connect and convert your tools into a functional spec. Once loaded into your agent, you simply prompt the system to search your indexed data.

You index past orders from `list_sales_orders` to build a repository of historical trends. The agent then performs semantic searches on this data to identify patterns.

You use the allowed_tools filter to restrict which endpoints the agent accesses. This gives you control over what the LlamaIndex agent can see and modify.

Include your live API data as a primary source in your RAG index. This forces the model to prioritize live returns over its internal training data.

Your order records are treated as transient context for the indexing process. They remain within your local memory or specified vector store, protected by your defined security policies.

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