January 12, 20268 MIN READ

Model Context Protocol (MCP): The Standard for AI Tool Integration

By Learnia Team

Model Context Protocol (MCP): The Standard for AI Tool Integration

This article is written in English. Our training modules are available in French.

In late 2024, Anthropic introduced the Model Context Protocol (MCP), an open standard for connecting AI assistants to external tools, data sources, and services. As AI agents become more capable and widely deployed, the need for standardized integration has become critical. MCP addresses this by providing a universal "language" for AI-to-tool communication.

This comprehensive guide explains MCP's architecture, implementation, and significance for AI development.

The Integration Problem

Before MCP

Every AI application built custom integrations:

┌─────────────────────────────────────────────────────┐
│              Custom Integration Chaos               │
├─────────────────────────────────────────────────────┤
│                                                     │
│  AI App 1 ──custom code──► Database A               │
│  AI App 1 ──custom code──► API B                    │
│  AI App 1 ──custom code──► Service C                │
│                                                     │
│  AI App 2 ──different code──► Database A            │
│  AI App 2 ──different code──► API B                 │
│  AI App 2 ──different code──► Service D             │
│                                                     │
│  Result: N apps × M services = N×M integrations     │
│                                                     │
└─────────────────────────────────────────────────────┘

Problems:

→Duplicated effort across applications
→Inconsistent implementations
→Hard to maintain
→Limited reusability
→Security varies widely

The MCP Solution

Standardize the connection layer:

┌─────────────────────────────────────────────────────┐
│              MCP Standardized Layer                 │
├─────────────────────────────────────────────────────┤
│                                                     │
│  AI App 1 ─┐                  ┌─► Database A        │
│            │                  │                     │
│  AI App 2 ─┼──► MCP Protocol ─┼─► API B             │
│            │                  │                     │
│  AI App 3 ─┘                  └─► Service C         │
│                                                     │
│  Result: N apps + M servers (via standard MCP)      │
│                                                     │
└─────────────────────────────────────────────────────┘

Benefits:

→Build once, use everywhere
→Consistent security model
→Community-maintained servers
→Plug-and-play capability
→Clear responsibility boundaries

MCP Architecture

Core Components

┌─────────────────────────────────────────────────────┐
│                  MCP Architecture                   │
├─────────────────────────────────────────────────────┤
│                                                     │
│  ┌─────────────┐         ┌─────────────┐            │
│  │   MCP HOST  │         │ MCP SERVER  │            │
│  │  (AI App)   │◄──────►│  (Service)  │            │
│  └─────────────┘  JSON   └─────────────┘            │
│        │          RPC           │                   │
│        │                        │                   │
│        ▼                        ▼                   │
│  ┌─────────────┐         ┌─────────────┐            │
│  │   CLIENT    │         │   SERVER    │            │
│  │  Library    │         │   Library   │            │
│  └─────────────┘         └─────────────┘            │
│                                                     │
└─────────────────────────────────────────────────────┘

MCP Host:

→The AI application (Claude Desktop, IDEs, etc.)
→Maintains connections to servers
→Routes requests from AI to appropriate server

MCP Client:

→Library within the host
→Handles protocol communication
→Manages server lifecycle

MCP Server:

→Exposes functionality via MCP
→Can be local or remote
→Provides tools, resources, or prompts

Three Capability Types

1. Tools Actions the AI can execute:

{
  "name": "search_database",
  "description": "Search the company database",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {"type": "string"},
      "limit": {"type": "integer"}
    }
  }
}

2. Resources Data the AI can read:

{
  "uri": "file:///data/reports/quarterly.pdf",
  "name": "Q4 Report",
  "mimeType": "application/pdf"
}

3. Prompts Reusable prompt templates:

{
  "name": "code_review",
  "description": "Structured code review prompt",
  "arguments": [
    {"name": "code", "required": true},
    {"name": "language", "required": false}
  ]
}

How MCP Works

Connection Flow

1. INITIALIZATION
   Host ──► Server: "initialize" request
   Server ──► Host: capabilities response
   Host ──► Server: "initialized" notification

2. DISCOVERY
   Host ──► Server: "list_tools" request
   Server ──► Host: available tools list

3. INVOCATION
   AI decides to use tool
   Host ──► Server: "call_tool" with arguments
   Server ──► Host: tool result

4. CLEANUP
   Host ──► Server: shutdown notification

Example: Database Tool

Server Implementation (Python):

from mcp.server import Server
from mcp.types import Tool, TextContent

server = Server("database-server")

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="query_users",
            description="Query user database",
            inputSchema={
                "type": "object",
                "properties": {
                    "filter": {"type": "string"},
                    "limit": {"type": "integer", "default": 10}
                }
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "query_users":
        results = await database.query(
            filter=arguments.get("filter"),
            limit=arguments.get("limit", 10)
        )
        return [TextContent(type="text", text=str(results))]

Host Configuration (Claude Desktop):

{
  "mcpServers": {
    "database": {
      "command": "python",
      "args": ["database_server.py"],
      "env": {
        "DATABASE_URL": "postgresql://..."
      }
    }
  }
}

Available MCP Servers

Official Servers

Anthropic provides reference implementations:

Server	Function
Filesystem	Read/write local files
GitHub	Repository operations
GitLab	GitLab integration
Slack	Slack messaging
Google Drive	Document access
PostgreSQL	Database queries
Puppeteer	Browser automation
Memory	Persistent memory

Community Servers

Growing ecosystem:

→Notion integration
→Linear (issue tracking)
→Obsidian (notes)
→Various APIs
→Custom enterprise tools

Finding Servers

Resources:

→GitHub: github.com/modelcontextprotocol
→MCP Registry: Community-maintained list
→npm/PyPI: Published packages

Building MCP Servers

Python SDK

# Basic MCP server in Python

import asyncio
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent

# Create server
app = Server("my-server")

# Define tools
@app.list_tools()
async def list_tools():
    return [
        Tool(
            name="greet",
            description="Generate a greeting",
            inputSchema={
                "type": "object",
                "properties": {
                    "name": {"type": "string"}
                },
                "required": ["name"]
            }
        )
    ]

# Implement tools
@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "greet":
        return [TextContent(
            type="text",
            text=f"Hello, {arguments['name']}!"
        )]

# Run server
async def main():
    async with stdio_server() as (read, write):
        await app.run(read, write)

asyncio.run(main())

TypeScript SDK

// Basic MCP server in TypeScript

import { Server } from "@modelcontextprotocol/sdk/server";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio";

const server = new Server({
  name: "my-server",
  version: "1.0.0"
});

// Define tools
server.setRequestHandler("tools/list", async () => ({
  tools: [{
    name: "calculate",
    description: "Perform calculation",
    inputSchema: {
      type: "object",
      properties: {
        expression: { type: "string" }
      }
    }
  }]
}));

// Implement tools
server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "calculate") {
    const result = eval(request.params.arguments.expression);
    return { content: [{ type: "text", text: String(result) }] };
  }
});

// Start server
const transport = new StdioServerTransport();
server.connect(transport);

Security Considerations

Trust Boundaries

┌─────────────────────────────────────────────────────┐
│                Security Boundaries                  │
├─────────────────────────────────────────────────────┤
│                                                     │
│  ┌───────────────────────────────────────────────┐  │
│  │              UNTRUSTED                        │  │
│  │  AI Model outputs (potentially adversarial)  │  │
│  └───────────────────────────────────────────────┘  │
│                        │                            │
│                        ▼                            │
│  ┌───────────────────────────────────────────────┐  │
│  │              MCP HOST                         │  │
│  │  - Validates tool calls                       │  │
│  │  - Enforces permissions                       │  │
│  │  - Logs actions                               │  │
│  └───────────────────────────────────────────────┘  │
│                        │                            │
│                        ▼                            │
│  ┌───────────────────────────────────────────────┐  │
│  │              MCP SERVER                       │  │
│  │  - Implements access controls                 │  │
│  │  - Validates inputs                           │  │
│  │  - Limits scope                               │  │
│  └───────────────────────────────────────────────┘  │
│                                                     │
└─────────────────────────────────────────────────────┘

Best Practices

For Server Developers:

→Validate all inputs strictly
→Implement least-privilege access
→Log all operations
→Handle errors gracefully
→Never trust AI-provided paths/URLs

For Host Administrators:

→Review server capabilities before enabling
→Configure appropriate permissions
→Monitor server activity
→Keep servers updated
→Isolate sensitive servers

Host Support

Claude Desktop

Native MCP support:

→Configure servers in settings
→Servers run locally
→Full tool/resource/prompt support

IDE Integrations

Growing support:

→VS Code extensions
→JetBrains plugins
→Custom IDE integrations

Custom Applications

Build your own:

→Use MCP client libraries
→Implement host logic
→Connect to any MCP servers

Future of MCP

Roadmap Items

Protocol Enhancements:

→Streaming responses
→Better error handling
→Authentication standards
→Remote server protocols

Ecosystem Growth:

→More official servers
→Enterprise integrations
→Certification program
→Enhanced discovery

Industry Adoption

MCP is positioned to become:

→Standard for AI integrations
→Required skill for AI developers
→Part of enterprise AI architecture

Key Takeaways

→
MCP is an open standard for connecting AI assistants to tools and data sources
→
Three capability types: tools (actions), resources (data), prompts (templates)
→
Architecture separates hosts (AI apps) from servers (capabilities)
→
SDKs available for Python and TypeScript development
→
Growing ecosystem of official and community servers
→
Security requires careful trust boundary management
→
Becoming standard for AI tool integration across the industry

Learn AI Agent Development

MCP is a key technology for building capable AI agents. Understanding how agents use tools—and how to build those integrations—is essential for modern AI development.

In our Module 6 — AI Agents & Orchestration, you'll learn:

→How AI agents reason and plan
→Tool integration patterns
→The ReAct framework
→Multi-agent orchestration
→Building safe, capable agents
→Error handling and recovery

These skills prepare you to build production-ready AI agents.

→ Explore Module 6: AI Agents & Orchestration

GO DEEPER

Module 6 — AI Agents & ReAct

Create autonomous agents that reason and take actions.

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