Model Context Protocol (MCP) – Why?

There’s been quite a bit of buzz lately around MCP, a new term in the AI space. Many are describing it as a kind of ‘USB-C’ for AI agents. So, what exactly is MCP, and why is it generating so much interest? We’ll be sharing our perspective on this topic in the following discussion.

The comparison to a USB-C port highlights MCP’s role in creating a consistent method for AI systems to interact with various tools and data. Certainly, the benefit of a unified connection to wider platforms is clear. However, it’s worth exploring whether this represents the full scope of MCP’s original intent.

Let’s take a step back and look at a breakdown of traditional AI workflows, their flaws and how MCP fills these gaps.

Traditional AI Workflow

We’ll use Customer Service Chatbot as an example here:

Step 1: Training

• Process: A chatbot is trained on historical customer service logs (text-only).
• Limitation:
  • Ignores real-time context (e.g., user emotion, location, past interactions).
  • Biases emerge from incomplete/outdated data (e.g., slang, cultural nuances).

Step 2: Deployment

• Process: The chatbot responds to queries using pre-defined rules or LLMs (e.g., GPT-4).
• Limitation:
  • Fails to adapt if a user is frustrated or switches languages mid-conversation.
  • Can’t prioritize urgent requests (e.g., “My order is missing” vs. “What’s your return policy?”).

Step 3: Output

• Process: Returns a generic response (e.g., “We apologize for the inconvenience.”).
• Limitation:
  • Doesn’t leverage user history (e.g., loyalty status, past complaints).
  • May offend non-native speakers due to lack of cultural context.

Pain Points of Traditional AI

Pain Point	Consequence	Example
Context Blindness	Models miss real-time signals (tone, location).	A chatbot can’t detect sarcasm in “Great service… NOT.”
Bias Amplification	Reinforces stereotypes from training data.	A resume-screening AI downgrades non-Western names.
Fragmented Multi-Modal Data	Text, voice, and sensor data processed in silos.	A self-driving car ignores pedestrian gestures because it only “sees” LiDAR.
Poor Adaptability	Fails in dynamic environments.	A fraud-detection model flags a legitimate overseas transaction as suspicious.
Black-Box Decisions	No audit trail for high-stakes outputs.	A loan-rejection AI can’t explain why it denied an applicant.

How MCP Solves These Gaps

Problem 1: Static Training

MCP Solution: Dynamic Context Injection:

• MCP feeds real-time data (e.g., user’s heartbeat from a smartwatch) to adjust responses.
• Example: A health-chatbot softens its tone if the user’s voice shows stress.

Problem 2: Bias

MCP Solution: Context-Aware Fairness Checks:

• Flags biased outputs and applies corrections (e.g., avoiding gendered assumptions in job recommendations).
• Example: An HR AI suggests “nurse” to male candidates after detecting historical bias.

Problem 3: Multi-Modal Silos

MCP Solution: Unified Context Engine:

• Combines text, voice, and sensor data (e.g., a retail AI uses camera + purchase history to recommend products).
• Example: A smart fridge AI notices expired milk via camera and adds it to your shopping list.

Problem 4: Black-Box Decisions

MCP Solution: Explainability Logs:

• Tracks how context influenced outputs (e.g., “Loan denied due to unstable employment context”).
• Example: A doctor sees why an AI suggested a diagnosis (e.g., “High fever + travel history → malaria risk”).

Real-World Impact

Without MCP	With MCP
A voice assistant mishears “Austin” as “Boston” due to accent bias.	Detects user’s Texan dialect and adapts speech recognition.
A fraud model blocks a valid transaction because it lacks travel-context.	Knows the user is on vacation and approves the purchase.
A medical AI recommends a standard treatment, ignoring patient allergies.	Cross-references EHR data to personalize recommendations.

Takeaways

Now let’s take another look at the “why”: traditional AI lacks real-world awareness, leading to rigid, biased and unreliable decisions. MCP bridges this gap by making AI context-aware—adapting dynamically, reducing bias, ensuring transparency and unifying multi-modal data. It transforms AI from a blunt tool into a precise and adaptive solution for real-world complexity.