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github/php-mcp-server-generator

공식 PHP SDK를 사용하여 도구, 리소스, 프롬프트 및 테스트가 포함된 완전한 PHP Model Context Protocol 서버 프로젝트를 생성합니다.

php-mcp-server-generator란 무엇인가요?

이 스킬은 개발자가 공식 PHP SDK를 활용하여 Model Context Protocol (MCP) 서버를 신속하게 부트스트래핑할 수 있도록 설계되었습니다. MCP는 AI 에이전트가 외부 도구, 리소스 및 데이터 소스와 상호작용할 수 있게 해주는 업계 표준 프로토콜입니다. 이 스킬은 완전한 디렉토리 구조, 사용자 정의 도구, 리소스, 프롬프트, 그리고 포괄적인 테스트 스위트를 포함한 프로젝트 템플릿을 생성합니다. PHP 생태계에 특화되어 있어 PHP 기반 AI 에이전트 인프라를 구축하는 개발자에게 특히 유용하며, Claude Code, Cursor, Cline 등 다양한 MCP 호환 클라이언트와 함께 사용할 수 있습니다. 자동화된 코드 생성, 테스트 주도 개발 지원, 모범 사례를 준수하는 확장 가능한 아키텍처를 제공합니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/php-mcp-server-generator

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

php-mcp-server-generator은(는) 무엇을 하나요?

공식 PHP SDK를 사용하여 도구, 리소스, 프롬프트 및 테스트가 포함된 완전한 PHP Model Context Protocol 서버 프로젝트를 생성합니다.

개요

이 스킬은 MCP(Model Context Protocol) 서버 프로젝트를 PHP로 생성하는 과정을 자동화합니다. MCP는 AI 에이전트가 안전하고 표준화된 방식으로 외부 시스템과 상호작용할 수 있도록 하는 프로토콜입니다.

사용법

  1. 새 디렉토리에서 이 스킬을 호출합니다.
  2. 프로젝트 이름과 네임스페이스를 제공합니다.
  3. 템플릿이 자동으로 완전한 프로젝트 구조를 생성합니다.

생성되는 구조

my-mcp-server/
├── src/
│   ├── Tools/
│   │   └── ExampleTool.php
│   ├── Resources/
│   │   └── ExampleResource.php
│   ├── Prompts/
│   │   └── ExamplePrompt.php
│   └── Server.php
├── tests/
│   ├── Tools/
│   │   └── ExampleToolTest.php
│   ├── Resources/
│   │   └── ExampleResourceTest.php
│   └── Prompts/
│       └── ExamplePromptTest.php
├── composer.json
├── phpunit.xml
└── README.md

주요 기능

  • 도구(Tools): PHP 메서드를 MCP 도구로 정의하여 AI 에이전트가 호출할 수 있습니다.
  • 리소스(Resources): 데이터 소스나 파일을 MCP 리소스로 노출합니다.
  • 프롬프트(Prompts): 미리 정의된 프롬프트 템플릿을 제공합니다.
  • 테스트(Test): PHPUnit을 사용한 완전한 테스트 스위트.

지원 플랫폼

  • Claude Code
  • Cursor
  • Codex
  • 기타 MCP 호환 클라이언트

시작하기

composer create-project my/mcp-server

자세한 내용은 공식 PHP MCP 문서를 참조하세요.

참고: 이 생성기는 최신 PHP SDK와 관행을 사용하여 프로젝트를 최신 상태로 유지합니다.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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