At Vortex IQ, we believe the future of software is agentic — where intelligent agents transform natural language into real business actions. This blog post unpacks how we made that a reality using our proprietary Model Context Protocol (MCP) Server, enabling any user to create AI agents directly from plain English instructions.

The Vision: Natural Language → Intelligent Action

Most startups are drowning in APIs but starving for automation. Traditional RPA and scripting tools fall short — they require technical effort, can’t reason, and don’t scale well across departments.

Our goal was simple:
Could a business user describe what they want, and have an AI agent automatically execute it using APIs — no code, no fuss?

The answer is yes. And it all starts with the MCP Server.

What is the MCP Server?

The Model Context Protocol (MCP) Server is our innovation layer that:

• Accepts natural language input from users 
• Translates it into structured agent intent 
• Maps the intent to a set of API workflows 
• Executes those workflows using authenticated, secure API calls 
• Returns live results, summaries, or completes actions autonomously 

Think of it as a bridge between what the user means and what the system does — without needing human developers in the loop.

The Build: Step-by-Step Breakdown

1. Schema Mapping for API Calls

We first created structured JSON schema files for the target API (e.g., BigCommerce, Shopify, Google Analytics), categorised into:

• number_fields 
• text_fields 
• boolean_fields 
• date_fields

Each field includes:

• A plain English description 
• Data type enforcement 
• Real examples from production

These schemas allowed our agents to reason about inputs, validate them, and auto-generate queries or payloads.

2. Prompt Compiler Engine

Next, we built a prompt compiler that transforms natural language into JSON intent objects.

Example input:

“Update the price of SKU ABC123 to £15.99 and make it visible in all channels.”

Output:

json

CopyEdit

{

  “action”: “update_product”,

  “sku”: “ABC123”,

  “price”: “15.99”,

  “visibility”: “all”

}

We use a hybrid of OpenAI models (GPT-4o) and domain-specific prompts to ensure reliability and determinism.

3. Agent Skill Builder

Each skill is a modular unit within the agent — essentially a microservice with a well-defined purpose (e.g., update stock, create discount, fetch analytics).

The MCP server connects skills to:

• HTTP APIs (REST, GraphQL) 
• Auth credentials (OAuth, API keys) 
• Condition flows (e.g., “if this fails, try fallback”) 

Skills are stackable — meaning agents can compose workflows like LEGO blocks.

4. Live Execution Layer

Once the intent is resolved, the MCP server routes it to the correct agent and skill set, handling:

• Auth tokens securely 
• Real-time response handling 
• Error fallback logic 
• Post-processing summaries 

Example: If an agent updates 300 SKUs, it returns a completion report, success/failure ratio, and suggested next steps.

Security & Control

We designed the entire platform to be enterprise-grade:

• All API keys and credentials are encrypted at rest and in transit 
• Role-based access control governs who can do what 
• Admins can sandbox actions before pushing to production 

Agents can also be deployed in staging environments, allowing teams to test AI-driven changes safely.

Real-World Use Case

A leading retail brand used our agent to:

• Optimise 1,200 product descriptions for SEO 
• Sync prices across 3 regional stores 
• Create discount campaigns for underperforming SKUs 

All of it was driven by conversational input, managed by our MCP server and executed via 5 chained agents.

What’s Next

We’re now building a no-code Agent Studio where:

• Anyone can create agents from prompt templates 
• Agents can be scheduled, triggered, or embedded in workflows 
• Partners can monetise their own agents in our Agent Marketplace 

We see a future where every team — from marketing to inventory — has their own agent, working 24/7.