MCP Architecture

MCP Architecture

Understanding MCP Architecture

The Model Context Protocol (MCP) is a standardized way for AI models to interact with external tools and data sources. Each MCP server in this project follows the JSON-RPC 2.0 specification and implements specific capabilities.

Core MCP Components

1. Tools

Functions that the AI can call to perform actions:

• Execute operations (file processing, calculations, etc.)
• Return structured results to the AI model
• Accept parameters for customization

2. Resources

Data sources that can be read or queried:

• Static files or dynamic data
• URI-based access patterns
• Support for different MIME types

3. Prompts

Pre-defined prompt templates for common tasks:

• Standardized interaction patterns
• Parameterized templates
• Context-aware suggestions

Protocol Structure

Server Initialization

# Standard MCP server setup
app = Server("server-name")

@app.list_tools()
async def handle_list_tools() -> list[Tool]:
    return [
        Tool(
            name="tool_name",
            description="Tool description",
            inputSchema={
                "type": "object",
                "properties": {
                    "param": {"type": "string"}
                }
            }
        )
    ]

Tool Implementation

@app.call_tool()
async def handle_call_tool(name: str, arguments: dict) -> list[TextContent]:
    if name == "tool_name":
        result = process_data(arguments["param"])
        return [TextContent(type="text", text=result)]

Communication Flow

1. Client Request: AI model requests available tools
2. Server Response: Server lists available tools with schemas
3. Tool Execution: AI calls specific tool with parameters
4. Result Return: Server processes and returns structured results

Best Practices

• Use clear, descriptive tool names
• Provide comprehensive input schemas
• Return structured, parseable results
• Implement proper error handling
• Follow async/await patterns for I/O operations