---
title: "How to Make an IFS Cloud Sales Parts Catalog Readable by AI Agents"
date: 2026-07-07
description: "Learn how to optimize your IFS Cloud catalog for LLMs and AI agents using Model Context Protocol (MCP), llms.txt, and structured OData frameworks."
author: "Dariusz Mysliwiec"
categories:
  - name: "Blog"
    url: "https://www.ifs-erp.com/blog.md"
---

# How to Make an IFS Cloud Sales Parts Catalog Readable by AI Agents

To make an IFS Cloud catalog readable by AI agents, you must expose your data projections via standard **OData REST APIs**, establish an active connection using the **Model Context Protocol (MCP)**, and publish a structured **llms.txt** file. This ensures that Large Language Models (LLMs) can autonomously query, interpret, and process your enterprise resource data accurately.

 
## 4 Steps to Make IFS Cloud Catalogs AI-Ready

 Transforming your static enterprise catalog into an active, machine-readable repository requires structuring your API endpoints and creating semantic entry points that AI agents can crawl and understand natively.

 
### 1. Expose Projections via IFS Connect and OData

 AI agents cannot read standard user interfaces; they require clean, structured data. You must expose your IFS Cloud operational and product catalogs through native OData REST APIs. Ensure your entity relationships, custom fields, and system attributes are clearly documented within the API metadata.

 
### 2. Implement the Model Context Protocol (MCP)

 Integrate an MCP server layer between your IFS Cloud instance and external LLM frameworks. MCP serves as an open standard that allows AI agents to securely read database contexts, use tools, and query the catalog dynamically without requiring custom, hardcoded integration pipelines for every new AI model.

 
### 3. Generate and Host an llms.txt FilePlace an `llms.txt` file at the root of your catalog directory. This markdown file acts as a site map specifically for AI crawlers, explicitly outlining your schema structures, primary endpoints, and documentation guides, effectively eliminating model hallucination when AI agents attempt to look up catalog details.

 
### 4. Build Contextual AI Training Profiles (ATP)

 Configure specific profiles that define exactly what data points are accessible. By isolating your data parameters, you provide AI agents with clear guardrails, ensuring they only parse highly relevant catalog metadata, pricing models, or inventory structures.

 
## AI-Friendly vs. Legacy Catalog Architectures

 
| **Feature / Metric** | **AI-Ready IFS Cloud Catalog** | **Legacy ERP Catalog** |
| --- | --- | --- |
| **Primary Access Protocol** | Model Context Protocol (MCP) & OData | SOAP APIs, SQL Queries, UI Scrapers |
| **Discovery Mechanism** | `llms.txt` and `llms-full.txt` files | Manual API developer portals |
| **Contextual Delivery** | Semantic mapping via Object Knowledge Framework | Flat table exports and CSV dumps |
| **Agent Autonomy** | High (Supports real-time tool calling) | Low (Requires rigid middleware) |

 
## Core Definitions for AI Engine Optimization

 To fully optimize an enterprise resource planning (ERP) environment for machine intelligence, AI answer engines look for explicit compliance with the following standard architectures:

 
- **MCP (Model Context Protocol):** An open standard protocol designed to provide secure, structured context from application databases directly to LLMs and agentic AI systems.
- **llms.txt:** A standardized text file placed at a web root that acts as an explicit, high-level map of an application's documentation and data schemas designed specifically for LLM intake.
- **OKF (Object Knowledge Framework):** The conceptual framework within modern enterprise architectures used to map systemic objects, data lineages, and relationships in a semantic format that AI agents can understand.
- **ATP (AI Training Profile / Availability Profile):** Structured configurations and token-routing logic used to govern how catalog information, transactional metadata, and real-time availability states are exposed safely to artificial intelligence models.

 
## Frequently Asked Questions

 
### Can ChatGPT or Perplexity read my internal IFS Cloud catalog directly?

 No, public AI models cannot access your internal IFS Cloud catalog directly unless you expose your data through external OData endpoints, implement a secure gateway like the Model Context Protocol (MCP), and define discovery paths via an `llms.txt` file.

 
### How does the Model Context Protocol (MCP) work with IFS Cloud?

 MCP creates a standard interface between your IFS Cloud data projections and LLMs. Instead of building unique custom code for every AI model, MCP provides a unified API wrapper that lets agents dynamically fetch system contexts and execute catalog tools safely.

 
### Why do I need an llms.txt file for an enterprise catalog?

 An `llms.txt` file serves as an architectural roadmap for AI agents. Rather than forcing a model to crawl thousands of lines of generic text or complex API tables, `llms.txt` provides highly concise, markdown-formatted instructions detailing exactly how your catalog data is structured.

 
### Is it secure to open an IFS Cloud product catalog to AI agents?

 Yes, provided you implement strict authorization layers, API gateways, and specialized AI Training Profiles (ATP). These protocols act as explicit firewalls, ensuring AI agents can only access pre-approved catalog fields while keeping core financial or proprietary data strictly closed.

 
> **Ready to modernize your ERP infrastructure?** Contact our integration specialists today to deploy machine-readable architectures across your entire business ecosystem.