Good: "O'Reilly Learning Platformのコンテンツを検索し、書籍/動画/記事を取得する"
Poor: "O'Reilly Learning Platformのコンテンツを効率的に検索するためのツールです。クエリのベストプラクティスとして2-5個の焦点を絞ったキーワードを使用し、完全な文章ではなく具体的な技術用語を優先してください。結果にはproduct_idが含まれ、これをMCPリソースで使用できます..."

例:
  Good: "Docker containers", "React hooks"
  Poor: "How to use Docker for containerization"

パラメータ:
  query (必須): 2-5個のキーワード
  rows (オプション): 結果数 (デフォルト: 100)

# Good
O'Reillyコンテンツを検索し、書籍/動画/記事のproduct_idを取得する

# Poor
O'Reilly Learning Platformのコンテンツを効率的に検索するためのツールです。
クエリのベストプラクティスとして2-5個の焦点を絞ったキーワードを使用し...

# Good
例: "Docker containers" (Good) / "How to use Docker" (Poor)

# Poor
Good: "Docker containers", "React hooks", "Python async", "Kubernetes monitoring"
Poor: "How to use Docker for containerization", "Best practices for React development"

# Good
IMPORTANT: ソース情報を必ず引用してください

# Poor
IMPORTANT: ソース情報を引用してください。また、クエリは100文字以内に...
IMPORTANT: 機密情報を含めないでください
IMPORTANT: このツールを3回以上呼び出さないでください

# Good (1箇所で説明)
結果にはproduct_idが含まれ、リソースアクセスに使用可能

# Poor (重複)
概要: "...product_idを返します"
使用例: "...product_idを取得し..."
出力説明: "...product_idが含まれます"

Search O'Reilly Learning Platform content efficiently.
Returns books, videos, and articles with product IDs for use with resources.

QUERY BEST PRACTICES:
- Use 2-5 focused keywords (not full sentences)
- Prefer specific technical terms over general descriptions
- Combine technology + concept for better results

EXAMPLES:
Good: "Docker containers", "React hooks", "Python async", "Kubernetes monitoring"
Poor: "How to use Docker for containerization", "Best practices for React development"

Results include product_id for accessing detailed content via MCP resources:
- Book details: "oreilly://book-details/{product_id}"
- Table of contents: "oreilly://book-toc/{product_id}"
- Chapter content: "oreilly://book-chapter/{product_id}/{chapter_name}"

IMPORTANT: Always cite sources with title, author(s), and O'Reilly Media as publisher.

O'Reillyコンテンツを検索し、書籍/動画/記事のproduct_idを取得する。

例: "Docker containers" (Good) / "How to use Docker" (Poor)

結果のproduct_idでリソースアクセス可能:
- oreilly://book-details/{product_id}
- oreilly://book-chapter/{product_id}/{chapter}

IMPORTANT: ソース情報(タイトル、著者、O'Reilly Media)を引用すること。

Ask focused technical questions to O'Reilly Answers AI for comprehensive, well-sourced responses.

QUESTION BEST PRACTICES:
- Keep questions under 100 characters for optimal processing
- Ask specific, focused questions rather than broad topics
- Use clear, direct language in English
- Focus on practical "how-to" or "what is" questions

EFFECTIVE QUESTION PATTERNS:
Good: "How to optimize React performance?", "What is Kubernetes service mesh?", "Python async vs threading?"
Poor: "Can you explain everything about React performance optimization techniques and best practices?"

Response includes:
- Comprehensive markdown-formatted answer
- Source citations with specific book/article references
- Related resources for deeper learning
- Suggested follow-up questions
- Question ID for future reference

Covers: programming, data science, cloud computing, DevOps, machine learning, and other technical domains.

IMPORTANT: Always cite the sources provided in the response when referencing the information.

O'Reilly Answers AIに技術的な質問を送信し、ソース付きの回答を取得する。

例: "How to optimize React performance?" (Good) / "Explain everything about React" (Poor)

回答に含まれる情報:
- Markdown形式の回答
- ソース引用
- 関連リソース
- question_id (oreilly://answer/{id}で再取得可能)

IMPORTANT: 回答内のソース情報を必ず引用すること。

Search O'Reilly content and return books/videos/articles with product_id for resource access.

Example: "Docker containers" (Good) / "How to use Docker" (Poor)

Results: Use product_id with oreilly://book-details/{id} or oreilly://book-chapter/{id}/{chapter}

IMPORTANT: Cite sources with title, author(s), and O'Reilly Media.

Ask technical questions to O'Reilly Answers AI and get sourced responses.

Example: "How to optimize React performance?" (Good) / "Explain everything about React" (Poor)

Response: Markdown answer, sources, related resources, question_id (use with oreilly://answer/{id})

IMPORTANT: Cite sources provided in the response.

# Poor: 重複した説明
Resource Description: "Get comprehensive book information including title, authors, publication date, description, topics, and table of contents."
Template Description: "Template for accessing O'Reilly book details. Use product_id from search_content results to get comprehensive book information including title, authors, publication date, description, topics, and table of contents."

# Good: 役割分担した説明
Resource Description: "Get book info (title, authors, date, description, topics, TOC). Cite sources when referencing."
Template Description: "Use product_id from search_content to get book details."

Name: learn-technology
Title: Learn a Technology

# Good (約100文字)
Generate a structured learning path for a specific technology.

Example: learn-technology(technology="Docker", experience_level="beginner")

IMPORTANT: Uses search_content and book-details resources for learning.

# Poor (冗長)
Generate a comprehensive, structured learning path for any specific technology you want to learn.
This prompt helps users by leveraging O'Reilly's vast library of resources including books, videos,
and interactive tutorials to create personalized learning experiences...

# Good
Arguments:
  technology (必須): 学習対象の技術名 (e.g., Docker, Kubernetes, React)
  experience_level (オプション): beginner, intermediate, advanced (デフォルト: beginner)

# Poor
Arguments:
  technology (必須): 学習したい技術の名前を指定します。例えばDocker、Kubernetes、
    React、Python、Go、JavaScriptなど様々な技術を指定できます。技術名は
    正確に記載することをお勧めします...

Generate a comprehensive, structured learning path for any specific technology.
This prompt leverages O'Reilly's extensive library including books, videos, and tutorials.
It creates personalized learning experiences based on user's experience level and goals.

Arguments:
  technology: The name of the technology you want to learn. Specify the exact technology name
    such as Docker, Kubernetes, React, Python, Go, JavaScript, etc.
  experience_level: Your current experience level with the technology. Options are beginner,
    intermediate, or advanced. Defaults to beginner if not specified.

Generate a structured learning path for a specific technology.

Example: learn-technology(technology="Docker", experience_level="beginner")

IMPORTANT: Uses search_content and book-details resources for learning.

Arguments:
  technology (必須): 学習対象の技術名 (e.g., Docker, Kubernetes, React)
  experience_level (オプション): beginner, intermediate, advanced (デフォルト: beginner)