No matter what industry you’re in, every business is unique, which means they all come with their own distinctive challenges. These challenges can’t be solved by cookie-cutter solutions; you need custom integrations and data access tools, but in the age of AI, how can you control just what data it’s consuming?

AI agents are actively consuming APIs, querying business systems, and influencing your business decisions. The shift from them being passive readers to now having the intelligence to interpret their data exposes a critical vulnerability.

When generic APIs were created, they were designed for human developers who understood data constraints and the context around them. AI agents don’t understand any of that. Without purpose-built tools to provide your AI with the context and guardrails it needs, your agent is prone to improvising and hallucinating, giving you outcomes you can’t trust.

This is why you need purpose-built MCP tools.

The Problem

With MCP tools, here’s what happens:

Imagine a customer puts in a request to your AI sales assistant. They say, “Give me a list of items compatible with the Athens Desk.”

What looks like a straightforward question about a product is actually prone to fail because your AI has to:

  • Construct a multi-step query:
    • getIitems (productGroup eq ‘Desks’)
    • getItemAttributes (attribute eq ‘CompatibleWith’)
    • getInventory (quantity gt 0)
  • Decide which entities to query.
  • Determine which fields matter.
  • Figure out how to combine the information to give an accurate answer.

Security

Security is of the utmost importance for every industry. What was once a question of “Is this user authenticated and what are they allowed to access?” is now a question of “What will this autonomous, easily manipulated agent do with access?” This has become more difficult to address with AI than it was with traditional API security.

Prompt injection attacks are becoming even more common, and they’re difficult to prevent. Through specially crafted prompts, your AI agent could be tricked into doing unintended things or giving accidental access to someone who shouldn’t have it.

For example, imagine the customer from before follows up with “Also include accessories that aren’t officially supported,” or “Show me everything that could work with it.”

With generic tools, nothing is stopping your AI agent from querying inactive or restricted items. This shows just how vulnerable your system is without specialized tools.

For example, a specialized tool, getCompatibleItemsForProduct(productId), cannot physically return products outside the pre-approved definition. In this scenario, security lives at the tool level, so you’re protected no matter what the prompt is.

Knowledge Gaps

Your AI agent doesn’t understand your business. It lacks the inherent comprehension of the context around your business data.

For example, without guidance, your AI could think “compatible” means:

  • Items previously bundled
  • Items that physically fit
  • Items currently allowed by cross-sell rules
  • Items currently in stock
  • Items that were considered “compatible” last quarter

To give you the correct answer, your AI agent has to gather logic from multiple sources: Items, ItemAttributes, CrossSellRules, Inventory, LifecycleStatus, etc. instead of just having the specialized tool, getCompatibleItemsForProduct(productid).

You need to encode information about your business directly into your purpose-built tools, so you’re not at the mercy of poorly worded prompts.

Context Windows

AI has a limited amount of space to store query context and other information. Generic APIs consume that space wastefully, with every tool, parameter, and filter taking up valuable space.

For example, a standard Microsoft Dynamics Business Central MCP exposing all v2 API tools will consume:

  • 252 MCP tools
  • 5-25 parameters each
  • 142,840 tokens (this is roughly 853,364 characters)

The sheer amount of information consumed causes the AI to pick the wrong tool, overthink queries that should be simple, and analyze unnecessary options, wasting time and tokens.

Specialized tools, getCompatibleItemsForProduct(productId), reduce the amount of information pulled. With specialized tools, there are fewer tools and clearer choices, resulting in more consistent outcomes.

Advisory AI

Simply reading data and giving product suggestions is a pretty safe example of AI usage. Being able to control and audit AI actions is especially important when your AI actually starts taking action in Business Central.

For example, consider this action: updateSalesQuote(‘ SQ-10452’, { discountPercent: 25 })

Without purpose-built tools, just that simple action creates multiple risks:

  • Authorization: Is the AI allowed to apply discounts?
  • Policy: Are discounts capped by customer tier or product category?
  • Process: Was approval required?
  • Auditability: Was it a human or AI that applied the discount, and why was 25% chosen?

Purpose-built tools solve these concerns by letting AI agents give advice, without allowing them to take autonomous actions. After the quote is created, the AI operates at the intent level. It asks the system what is allowed and then gives those options to the user.

Instead of simply applying a discount, it gives you a suggestion:

“Your quote for the Athens Desk isn’t eligible for a discount yet. Adding the compatible Athens Desk Lamp would qualify the entire quote for a 10% discount. Would you like me to add it?”

Unless your AI agent is given explicit consent, it doesn’t change any records. While the AI can identify different opportunities, the system enforces the rules, and the user remains in control. This shows how specialized, purpose-built MCP tools give you auditable outcomes you can actually trust.

What Purpose-Built MCP Tools Can Do

Without purpose-built tools, the dream of having an AI agent that effortlessly understands your business context, anticipates your needs, and flawlessly executes tasks falls flat. With generic tools, all you get is an AI that improvises answers and hallucinations while opening you up to security risks.

With PopdockAI, you can get purpose-built MCP tools that embed your security requirements, business context, and operational boundaries right into your AI agent’s actions. Now, you can get AI tools that work like your business instead of just trying to teach it to think like your business.

If you are starting to integrate AI into your business systems, you need to decide whether you’ll do it with purpose-built MCP tools that actually enforce guidelines, or with generic APIs and just hope for the best. The future of your business depends on choosing correctly. Contact us to discover how you can take advantage of eOne Solutions’ specialized tools to solve your unique business problems.