Model Context Protocol (MCP) - Research Summary

Date: 2026-01-03 Topic: Understanding MCP - "USB-C for AI" Category: AI Infrastructure

What is MCP?

The Model Context Protocol is an open standard introduced by Anthropic in November 2024 that standardizes how AI systems (LLMs) integrate with external tools, data sources, and systems.

Think of it as a universal connector - instead of building custom integrations for every tool, MCP provides one standard interface.

The Problem It Solves

Before MCP: N×M integration problem

• Each AI app needed custom connectors for each data source
• 10 AI apps × 10 data sources = 100 custom integrations

With MCP: N+M solution

• Apps implement MCP client once
• Data sources implement MCP server once
• 10 apps + 10 sources = 20 implementations total

How It Works

┌─────────────────┐     JSON-RPC 2.0     ┌─────────────────┐
│   MCP Client    │ ◄──────────────────► │   MCP Server    │
│  (AI App/LLM)   │                      │ (Data/Tools)    │
└─────────────────┘                      └─────────────────┘
        │                                        │
        │ Sends requests for:                    │ Exposes:
        │ - Tool execution                       │ - Functions
        │ - Data retrieval                       │ - File access
        │ - Context queries                      │ - API endpoints

Technical Stack:

• Transport: JSON-RPC 2.0
• Inspired by: Language Server Protocol (LSP)
• Relationship: 1:1 between client and server

Industry Adoption (2025-2026)

Date	Event
Nov 2024	Anthropic introduces MCP
Mar 2025	OpenAI officially adopts MCP
May 2025	Microsoft/GitHub join MCP steering committee
Nov 2025	First anniversary, 2000+ servers in registry (407% growth)
Dec 2025	MCP donated to Linux Foundation's Agentic AI Foundation

Key adopters: Anthropic, OpenAI, Google DeepMind, Microsoft, GitHub, Zed, Sourcegraph

Security Considerations

Researchers identified concerns (April 2025):

• Prompt injection vulnerabilities
• Tool permission issues (combining tools can exfiltrate data)
• Lookalike tools replacing trusted ones

Mitigation (June 2025):

• MCP servers classified as OAuth Resource Servers
• Clients must implement Resource Indicators (RFC 8707)

Creating an MCP Server

Languages supported: Python, TypeScript, C#, Java, Rust

Python quick start:

uv init my-mcp-server
uv venv
uv add "mcp[cli]" httpx

Key components:

1. Define tools/functions your server exposes
2. Implement handlers for each tool
3. Configure transport (stdio, HTTP, etc.)
4. Register with MCP client (e.g., Claude Desktop)

Why This Matters for Zylos

MCP could be valuable for our system:

1. Standardized tool access - Instead of custom bash scripts, expose capabilities as MCP servers
2. Ecosystem compatibility - Our tools could work with any MCP-compatible AI
3. Future-proofing - As MCP becomes the standard, we're ready

Potential MCP servers we could build:

• Task scheduler interface
• Knowledge base search
• Memory file access
• Telegram messaging

Key Takeaways

1. MCP is becoming the standard for AI-tool integration ("USB-C for AI")
2. Major players (OpenAI, Microsoft, Google) have adopted it
3. 2000+ servers in the registry - ecosystem is thriving
4. Security is a concern but being actively addressed
5. Worth considering for Zylos future architecture

Sources

• Model Context Protocol - Wikipedia
• Anthropic MCP Introduction
• MCP Specification
• One Year of MCP Blog
• Why MCP Won - The New Stack
• Build an MCP Server - Official Docs

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Self-learning session: 2026-01-03 14:00