xenitV1

mcp-builder

@xenitV1/mcp-builder
xenitV1
79
15 forks
Updated 1/6/2026
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MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

Installation

$skills install @xenitV1/mcp-builder
Claude Code
Cursor
Copilot
Codex
Antigravity

Details

Pathskills/mcp-builder/SKILL.md
Branchmain
Scoped Name@xenitV1/mcp-builder

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions


name: mcp-builder description: MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

MCP Builder

Principles for building MCP servers.


1. MCP Overview

What is MCP?

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

Core Concepts

ConceptPurpose
ToolsFunctions AI can call
ResourcesData AI can read
PromptsPre-defined prompt templates

2. Server Architecture

Project Structure

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

Transport Types

TypeUse
StdioLocal, CLI-based
SSEWeb-based, streaming
WebSocketReal-time, bidirectional

3. Tool Design Principles

Good Tool Design

PrincipleDescription
Clear nameAction-oriented (get_weather, create_user)
Single purposeOne thing well
Validated inputSchema with types and descriptions
Structured outputPredictable response format

Input Schema Design

FieldRequired?
TypeYes - object
PropertiesDefine each param
RequiredList mandatory params
DescriptionHuman-readable

4. Resource Patterns

Resource Types

TypeUse
StaticFixed data (config, docs)
DynamicGenerated on request
TemplateURI with parameters

URI Patterns

PatternExample
Fixeddocs://readme
Parameterizedusers://{userId}
Collectionfiles://project/*

5. Error Handling

Error Types

SituationResponse
Invalid paramsValidation error message
Not foundClear "not found"
Server errorGeneric error, log details

Best Practices

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

6. Multimodal Handling

Supported Types

TypeEncoding
TextPlain text
ImagesBase64 + MIME type
FilesBase64 + MIME type

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

8. Configuration

Claude Desktop Config

FieldPurpose
commandExecutable to run
argsCommand arguments
envEnvironment variables

9. Testing

Test Categories

TypeFocus
UnitTool logic
IntegrationFull server
ContractSchema validation

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

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