Difference between revisions of "Model Context Protocol"

Revision as of 14:41, 15 April 2025

Full Title or Meme

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.^[1]

Context

Quantum computers are expected to break traditional cryptographic systems like RSA and ECC. In response,

MCP is a security nightmare!

Don’t get me wrong — at Wexa, we see Model Context Protocol as the HTTP of the GenAI era. It’s the missing link to make AI assistants truly agentic and interoperable.

Problems

MCP doesn’t govern security by default. -> Tool poisoning attacks are a cakewalk if you’re not explicitly validating or sanitizing inputs. Anyone can sneak in malicious instructions through tool descriptions or context — and the model will just obey.

Versioning is a mess. -> There’s no clear standard for how models or tools should handle different MCP versions. That means you risk breaking tools silently — or worse, running outdated logic with no warning.

Persistent context = persistent risk. -> If your AI coworker remembers things across sessions, what happens when the context itself is compromised or manipulated?

We’re bullish on MCP — it’s foundational to how we’re building secure, scalable AI agents inside Wexa.

if you’re using MCP in production today, you’re likely exposed unless you’ve already wrapped it in a tight security layer.

MCP is powerful. But don’t assume it’s safe out of the box.

Solutions

• 2025-04-11 [arxiv:2504.08623v1 Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies]
• 2025-04 Google joins OpenAI in adopting Anthropic's protocol for connecting AI agents - why it matters
• 2024-03-19 Introducing Model Context Protocol (MCP) in Copilot Studio: Simplified Integration with AI Apps and Agents Microsoft
• Xinyi Hou, Yanjie Zhao, Shenao Wang, Haoyu Wang Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 2025-03-30 https://arxiv.org/abs/2503.23278
• 2024-11-25 Introducing the Model Context Protocol

Enable AI Agent

Difference between RAG & MCP

The difference between RAG (Retrieval-Augmented Generation) and MCP (Model Context Protocol) lies in their approach to data retrieval and usage in AI systems:

• RAG (Retrieval-Augmented Generation):
  • RAG fetches external data in real-time to augment the AI's responses. For example, it might query a database or API to retrieve the latest information before generating an answer.
  • It is highly flexible, allowing AI to access diverse sources for context-specific queries. However, it can be resource-intensive, as each query requires external connections.
• MCP (Model Context Protocol):
  • MCP focuses on maintaining context within the AI itself, enabling it to remember previous interactions and provide personalized responses.
  • It reduces the need for external data retrieval, relying on stored memory and predefined prompts.
  • MCP is more efficient for workflows requiring continuity and real-time data integration.

In essence, RAG is like fetching fresh data for every query, while MCP is about leveraging stored context for smarter, seamless interactions

References

1. ↑ Get started with the Model Context Protocol (MCP) https://modelcontextprotocol.io/introduction

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