What is the Model Context Protocol?
Imagine a world where every electronic device required a different type of connector — one phone charges via Micro-USB, another with a proprietary brand-specific plug, and a third with something else entirely. That was the situation before USB-C. And it's exactly the situation in AI integration today — at least without MCP.
The Model Context Protocol (MCP) is an open standard introduced by Anthropic (the company behind Claude AI) in late 2024. Its goal is simple yet ambitious: to provide a unified way for AI models to access external tools and data sources — whether that's a CRM system, an email account, a calendar, an invoicing program, or anything else.
In short: MCP is the USB-C of AI integration.
Why Do We Need It? — The Problem It Solves
Integration Chaos
In 2025, the average company uses 10–15 different software tools. If we want to connect AI to all of them, every integration must be built individually:
- OpenAI + Gmail? → Custom code.
- OpenAI + Salesforce? → Different custom code.
- Claude + Gmail? → Yet another custom code.
- Claude + Salesforce? → And another one.
With N AI models and M tools, that means N × M integrations. With 5 AI models and 10 tools, that's 50 different integrations. Every update, every API change requires rewriting the affected connectors.
The MCP Solution
MCP reduces the N × M problem to N + M:
WITHOUT MCP WITH MCP
AI Models Tools AI Models Tools
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│ GPT-4o │────│ Gmail │ │ GPT-4o │ │ Gmail │
│ │╲ │ │ │ │╲ │ │
│ │ ╲──│Calendar│ │ │ ╲ │Calendar│
│ │╲ ╲─│Salesforce│ │ │ ╲ │Salesforce│
└────────┘ ╲ └────────┘ └────────┘ │ └────────┘
┌────────┐ ╲ ┌────────┐ ┌────────┐ │ ┌────────┐
│ Claude │────│ Gmail │ │ Claude │──MCP──│ Gmail │
│ │╲ ╱─│Calendar│ │ │ │ │Calendar│
│ │ ╱──│Salesforce│ │ │ ╱ │Salesforce│
└────────┘╱ └────────┘ └────────┘╱ └────────┘
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│ Gemini │────│ Gmail │ │ Gemini │ │ │
│ │────│Calendar│ │ │ │ │
└────────┘ └────────┘ └────────┘ └────────┘
= 9 integrations = 3 + 3 = 6 (and it scales!)
Every AI model speaks to MCP the same way, and every tool connects to MCP the same way. Adding a new tool means writing one connector, not N of them.
How Does It Work? — MCP in 3 Minutes
MCP is built around three core concepts:
Tools
Functions that the AI can call. Each tool has:
- A name: e.g.
gmail_send,calendar_create_event - A description: in natural language, so the AI knows when to use it
- Parameters: what data it expects (recipient, subject, date, etc.)
When a user says "Send an email to Johnson about the proposal", the AI recognizes it needs the gmail_send tool, fills in the parameters, and calls it.
Resources
Data that the AI can read as context — documents, database records, files. Resources aren't actions; they're information: the AI reads them to give better answers.
Prompts
Pre-defined instruction templates that guide the AI toward correct behavior. For example: "Before sending an email, always confirm the recipient and content with the user."
How MCP Works in Practice
User: "Send Johnson a reminder about tomorrow's meeting"
│
▼
┌────────────────────────────┐
│ AI Model │
│ 1. Understands intent │
│ 2. Selects tool │
│ → gmail_send │
│ 3. Fills parameters │
│ → to: johnson@co.com │
│ → subject: Reminder │
└────────────┬───────────────┘
│ MCP protocol
▼
┌────────────────────────────┐
│ Gmail Connector │
│ (MCP Server) │
│ → OAuth2 authentication │
│ → Gmail API call │
│ → Email sent │
└────────────────────────────┘
The key insight: the AI model doesn't need to know how the Gmail API works. It just calls the MCP tool — the connector handles the rest.
Who Supports It and Where Does the Market Stand?
The Major Players
MCP was embraced by the industry's biggest names in 2025–2026:
- Anthropic — Creator of MCP; Claude AI's entire ecosystem is built on MCP
- OpenAI — Announced MCP support in ChatGPT and the Agents SDK in 2025
- Google DeepMind — MCP-compatible tool-use interface for Gemini models
- Microsoft — Copilot Studio supports MCP connectors
- Cursor, Windsurf — AI-powered code editors extending capabilities via MCP
The Ecosystem Scale
By early 2026, thousands of MCP connectors are available as open source:
- Google Workspace (Gmail, Calendar, Drive)
- GitHub, GitLab, Jira, Linear
- Slack, Discord, Microsoft Teams
- Stripe, Shopify, invoicing systems
- Databases (PostgreSQL, MongoDB, Supabase)
This "network effect" makes MCP truly powerful: the more connectors exist, the more valuable the standard becomes.
MCP Among the Trends
Practical Examples — MCP in Action
Service Sector (salon, clinic, consulting firm)
The owner enables the Gmail and Google Calendar connectors. From that point, the AI assistant can:
- Search emails: "What did Mrs. Miller write last?" → The AI searches using the Gmail MCP tool
- Create calendar events: "Book an appointment with Smith for Friday" →
calendar_create_event - Send emails (with approval): "Send a reminder to tomorrow's patients" →
gmail_send
The key: no development required. Enabling a connector is a configuration step — the AI immediately gains new capabilities.
Sales Team
Through MCP, the AI accesses the CRM, email, and calendar simultaneously:
"Prepare a summary of the Acme Corp deal — last emails, open proposal, next meeting."
The AI:
- CRM tool → deal data, pipeline status
- Gmail MCP → last email thread with Acme Corp
- Calendar MCP → next calendar event involving Acme Corp
- Natural language summary of everything
Invoice Automation (future vision)
When connectors for invoicing platforms become available:
"Issue an invoice to Acme Corp for the last project"
The AI pulls deal data from the CRM, creates an invoice draft via the invoicing MCP tool, and issues it after approval. Today this is a 5–10 minute manual process — with MCP it's 15 seconds plus one click.
MCP vs. Previous Solutions
MCP doesn't necessarily replace Zapier or Make — those remain excellent for fixed, repetitive workflows. MCP's strength lies where flexibility is needed: the AI decides context-dependently which tool to use and with what parameters.
Security and Data Protection
The most common concern about MCP is legitimate: "If the AI has access to my Gmail, who can see my emails and data?"
MCP's Security Model
OAuth2-based authorization: The user personally grants access (e.g., "Do you allow this application to read and send emails?"). The admin doesn't hand over a password — the user maintains control.
Scope limitation: Each connector can only access what it was granted permission for. The Gmail connector can't access Drive, and the Calendar connector can't read email.
Approval for sensitive operations: MCP allows read operations to be automatic while requiring human approval for write operations (sending emails, creating events). This isn't a limitation — it's a design pattern.
Tenant isolation: In multi-client systems, each user/company can only access their own connectors and data. The AI never "mixes up" two clients' contexts.
GDPR Considerations
- Data reaching the LLM through the MCP connector is not stored in the model's memory (provided the business agreement guarantees this)
- The user can revoke connector permissions at any time — from that point, the AI loses access
- Audit logs record which AI actions used which data — this is the foundation for transparency and accountability
What Does This Mean in Practice?
If You're a Business Leader
MCP means that integrating AI into existing systems won't be a years-long, enterprise-grade project but a matter of days or weeks. As the connector ecosystem grows, more tools become available to AI — without development.
If You're a CTO / IT Director
MCP is a vendor-agnostic open standard. If you're using OpenAI today and switch to Claude tomorrow — the MCP connectors stay the same. This drastically reduces vendor lock-in risk.
If You're a Developer
The MCP specification is open, and the reference implementation is available in TypeScript and Python. Writing a new connector (e.g., for your company's internal system) typically takes 1–3 days — and from that point, any AI model can use it.
The Key Takeaway
MCP isn't just another technology buzzword — it's an infrastructure turning point. Just as USB-C unified charging and data transfer, MCP unifies communication between AI and the external world.
Companies that start adopting MCP-compatible connectors now aren't just integrating with one AI model — they're integrating with every future AI model.
This article is based on the MCP specification (2025), experience from the AIMY project's connector system, and 2026 market trends.
If you're considering a similar solution, get in touch with us!