MCP Builder

Principles for building MCP servers.

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1. MCP Overview

What is MCP?

Model Context Protocol - standard for connecting AI systems with external tools and data sources.

Core Concepts

Concept	Purpose
Tools	Functions AI can call
Resources	Data AI can read
Prompts	Pre-defined prompt templates

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2. Server Architecture

Project Structure

my-mcp-server/
├── src/
│   └── index.ts      # Main entry
├── package.json
└── tsconfig.json

Transport Types

Type	Use
Stdio	Local, CLI-based
SSE	Web-based, streaming
WebSocket	Real-time, bidirectional

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3. Tool Design Principles

Good Tool Design

Principle	Description
Clear name	Action-oriented (get_weather, create_user)
Single purpose	One thing well
Validated input	Schema with types and descriptions
Structured output	Predictable response format

Input Schema Design

Field	Required?
Type	Yes - object
Properties	Define each param
Required	List mandatory params
Description	Human-readable

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4. Resource Patterns

Resource Types

Type	Use
Static	Fixed data (config, docs)
Dynamic	Generated on request
Template	URI with parameters

URI Patterns

Pattern	Example
Fixed	docs://readme
Parameterized	users://{userId}
Collection	files://project/*

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5. Error Handling

Error Types

Situation	Response
Invalid params	Validation error message
Not found	Clear "not found"
Server error	Generic error, log details

Best Practices

• Return structured errors
• Don't expose internal details
• Log for debugging
• Provide actionable messages

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6. Multimodal Handling

Supported Types

Type	Encoding
Text	Plain text
Images	Base64 + MIME type
Files	Base64 + MIME type

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7. Security Principles

Input Validation

• Validate all tool inputs
• Sanitize user-provided data
• Limit resource access

API Keys

• Use environment variables
• Don't log secrets
• Validate permissions

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8. Configuration

Claude Desktop Config

Field	Purpose
command	Executable to run
args	Command arguments
env	Environment variables

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9. Testing

Test Categories

Type	Focus
Unit	Tool logic
Integration	Full server
Contract	Schema validation

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10. Best Practices Checklist

• Clear, action-oriented tool names
• Complete input schemas with descriptions
• Structured JSON output
• Error handling for all cases
• Input validation
• Environment-based configuration
• Logging for debugging

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Remember: MCP tools should be simple, focused, and well-documented. The AI relies on descriptions to use them correctly.