Understanding the Model Context Protocol

The Model Context Protocol (MCP) is a standard for connecting AI models to external tools and data sources. It defines how AI systems discover, authenticate, and execute tools.

Why MCP Exists

AI models operate on text and probabilities. They cannot directly:

Access current information
Perform calculations
Interact with external systems
Maintain state between conversations
Execute code

Without external capabilities, AI remains limited to conversation and analysis. With tools, AI becomes an active participant in work.

MCP provides a standardized way for AI systems to use tools safely and reliably.

Core Components

Tools

Tools are functions that AI models can call. Each tool has:

Name: Identifier for the tool
Description: What the tool does
Input Schema: Structure of data the tool accepts
Output: Results the tool returns

Example tool definition:

{
  "name": "get_weather",
  "description": "Get current weather for a location",
  "inputSchema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City or location name"
      }
    },
    "required": ["location"]
  }
}

Resources

Resources are data sources that AI can read. Unlike tools, resources provide information without requiring input parameters.

Examples:

File contents
Database records
API responses
Documentation

Prompts

Prompts are predefined conversation starters or templates. They help AI systems handle specific types of requests consistently.

How MCP Works

MCP uses client-server architecture:

MCP Client: The AI system (Claude, ChatGPT, etc.)
MCP Server: Provides tools, resources, and prompts
Transport: JSON-RPC over HTTP, WebSockets, or stdio

Communication follows this pattern:

AI System → MCP Server: "What tools are available?"
MCP Server → AI System: Tool definitions
AI System → MCP Server: "Execute tool X with parameters Y"
MCP Server → AI System: Tool results

Transport Methods

HTTP/WebSockets

Tools exposed as web endpoints
Supports remote access
Enables cloud-based tool distribution
Requires authentication and authorization

Standard I/O

Tools run as local processes
Direct communication via stdin/stdout
Common for development and testing
No network overhead

Why MCP Matters

Standardization

Before MCP, every AI system implemented tool integration differently:

Custom APIs for each provider
Incompatible authentication methods
Varying tool definition formats
Proprietary protocols

MCP provides a common language that all AI systems can understand.

Tool Discovery

MCP enables automatic tool discovery. AI systems can:

Query available tools dynamically
Understand tool capabilities from descriptions
Validate inputs against schemas
Handle errors consistently

Security

MCP includes built-in security considerations:

Tools run in isolated environments
Explicit permission models
Audit trails for tool usage
Resource access controls

Ecosystem Growth

Standardization creates network effects:

Tool developers can build once, deploy everywhere
AI systems gain access to more tools
Organizations can share tools internally
Commercial tool marketplaces become viable

Real-World Applications

Business Intelligence

// Tool that queries sales database
export default async function getSalesData(input: {
  startDate: string;
  endDate: string;
  region?: string;
}) {
  // Query database, return results
  return {
    totalRevenue: 1250000,
    orderCount: 450,
    topProducts: [...]
  };
}

AI can now answer: "What's our sales performance this quarter?"

Content Creation

// Tool that generates images
export default async function generateImage(input: {
  prompt: string;
  style?: string;
}) {
  // Call image generation API
  return {
    imageUrl: "https://...",
    metadata: {...}
  };
}

AI can create visual content as part of workflows.

Data Processing

// Tool that processes spreadsheets
export default async function analyzeSpreadsheet(input: {
  fileUrl: string;
  analysis: string;
}) {
  // Download and analyze file
  return {
    summary: "...",
    insights: [...],
    charts: [...]
  };
}

AI can work with user data directly.

Enterprise Implications

Tool Governance

Organizations can:

Centralize tool development and deployment
Maintain consistent security policies
Track tool usage and performance
Version control tool implementations

Integration

MCP enables seamless integration with:

Existing enterprise systems
Internal APIs and databases
Third-party services
Legacy applications

Compliance

Standardized protocols support:

Audit requirements
Access controls
Data governance
Regulatory compliance

Technical Implementation

Tool Definition

Tools are defined using JSON Schema:

{
  "name": "calculate_roi",
  "description": "Calculate return on investment for a project",
  "inputSchema": {
    "type": "object",
    "properties": {
      "initialInvestment": {
        "type": "number",
        "description": "Initial amount invested"
      },
      "finalValue": {
        "type": "number",
        "description": "Final value after investment"
      },
      "timePeriod": {
        "type": "number",
        "description": "Time period in years"
      }
    },
    "required": ["initialInvestment", "finalValue", "timePeriod"]
  },
  "outputSchema": {
    "type": "object",
    "properties": {
      "roi": {
        "type": "number",
        "description": "Return on investment as percentage"
      },
      "profit": {
        "type": "number",
        "description": "Total profit amount"
      }
    }
  }
}

Error Handling

MCP includes standardized error responses:

{
  "error": {
    "code": -32602,
    "message": "Invalid parameters",
    "data": {
      "details": "location parameter is required"
    }
  }
}

Streaming

Tools can stream results as they become available:

Event: tool_output
Data: {"partial": "Processing", "progress": 0.1}

Event: tool_output
Data: {"partial": " data...", "progress": 0.5}

Event: tool_output
Data: {"result": {"final": "Complete result"}, "progress": 1.0}

MCP and Dwizi

Dwizi makes MCP accessible by:

Simplifying Tool Creation: Write functions, get MCP-compatible endpoints
Handling Infrastructure: No servers, containers, or scaling to manage
Providing Security: Built-in isolation and resource limits
Enabling Distribution: Tools become URLs that work anywhere

Without Dwizi, implementing MCP requires:

Understanding protocol specifications
Building server infrastructure
Managing security and isolation
Handling scaling and reliability

With Dwizi, you write a function and get an MCP server.

The Future of MCP

MCP represents a fundamental shift in AI architecture. Instead of AI systems that can only converse, we get AI systems that can act.

This enables:

AI as a programming paradigm
Automated workflows and processes
AI-human collaboration at scale
Tool ecosystems that enhance AI capabilities

MCP is not just a protocol—it's the foundation for practical AI systems.