Google Search Skill

Overview

This Skill enables Claude to perform Google searches using the google_search_tool command-line interface. It retrieves current web information, making it ideal for answering questions that require up-to-date data, recent news, or information beyond Claude's knowledge cutoff.

When to Use This Skill

Use this Skill when:

• Users ask for current events, news, or recent information
• Questions require the latest technical documentation or API references
• Looking up current prices, statistics, or data
• Finding resources, tutorials, or guides on specific topics
• Verifying facts or getting multiple perspectives from web sources
• Users explicitly request a web search or Google search

Instructions

When performing a web search, follow these steps:

1. Identify the Search Query

• Extract the key search terms from the user's request
• Formulate a clear, concise search query
• Use specific keywords for better results
• Include relevant technical terms, product names, or specific phrases

2. Execute the Search

Run the search using the google_search_tool:

python -m google_search_tool "your search query" --pretty

Options:

• Use --pretty for formatted JSON output (easier to read)
• Use -n <number> to specify number of results (1-10, default: 10)
• For focused searches, use -n 3 or -n 5

Examples:

# General search with 5 results
python -m google_search_tool "Python 3.13 new features" -n 5 --pretty

# Focused search with 3 results
python -m google_search_tool "React hooks tutorial" -n 3 --pretty

# Full search with 10 results
python -m google_search_tool "machine learning frameworks comparison" --pretty

3. Parse and Present Results

After receiving the search results:

1. Check for errors: If the output contains "error", explain the error to the user
2. Extract key information: Parse the JSON output for title, link, and snippet
3. Summarize findings: Provide a concise summary of what you found
4. Present sources: List relevant results with titles and links
5. Answer the question: Use the search results to answer the user's original question

Presentation format:

Based on my search for "[query]", here's what I found:

[Summary of findings based on search results]

Key resources:
1. **[Title 1]** - [Brief description from snippet]
   [URL]

2. **[Title 2]** - [Brief description from snippet]
   [URL]

[Additional context or recommendations]

4. Handle Edge Cases

No results found:

{
  "results": [],
  "count": 0
}

• Inform the user that no results were found
• Suggest trying different search terms
• Offer to search with alternative queries

API error:

{
  "error": "Error message"
}

• Check if environment variables are set (GOOGLE_API_KEY, GOOGLE_CSE_ID)
• Explain the error to the user
• Suggest troubleshooting steps if applicable

Timeout or network error:

• Inform the user of the issue
• Offer to retry the search
• Suggest checking network connectivity

5. Follow-up Searches

If the initial search doesn't fully answer the question:

• Refine the search query based on initial results
• Perform additional targeted searches
• Combine information from multiple searches

Examples

Example 1: Current Events

User request: "What are the latest developments in AI this week?"

Steps:

1. Search: python -m google_search_tool "latest AI developments 2025" -n 5 --pretty
2. Parse the JSON results
3. Summarize key developments from the snippets
4. Present with source links

Example 2: Technical Documentation

User request: "How do I use React Server Components?"

Steps:

1. Search: python -m google_search_tool "React Server Components guide" -n 5 --pretty
2. Identify official documentation and tutorials
3. Extract key concepts from snippets
4. Provide overview with links to detailed resources

Example 3: Comparison Research

User request: "Compare PostgreSQL vs MySQL for large-scale applications"

Steps:

1. Search: python -m google_search_tool "PostgreSQL vs MySQL large scale comparison" -n 5 --pretty
2. Gather multiple perspectives from results
3. Synthesize comparisons from different sources
4. Present balanced view with source attribution

Important Notes

Environment Setup

The google_search_tool requires:

• GOOGLE_API_KEY: Google Cloud API key
• GOOGLE_CSE_ID: Custom Search Engine ID
• These should be set in the .env file in the project root

Limitations

• Maximum 10 results per search
• API rate limits apply (check Google CSE quotas)
• Results depend on Google's indexing and ranking
• Snippets are truncated (may not contain full context)

Best Practices

1. Be specific: Use precise search terms for better results
2. Verify sources: Check that URLs and snippets are relevant before citing
3. Cite properly: Always include source URLs when presenting information
4. Respect recency: Recent results may be more relevant for time-sensitive queries
5. Multiple searches: For complex topics, perform several targeted searches rather than one broad search

Troubleshooting

If searches fail:

1. Verify the google_search_tool is installed: pip show google-search-mcp
2. Check environment variables are set in .env
3. Test manually: python -m google_search_tool "test query" --pretty
4. Review API quota limits in Google Cloud Console
5. Check network connectivity

Integration with Other Tools

This Skill works well alongside:

• Read tool: After finding URLs, read local documentation
• WebFetch tool: Retrieve full content from found URLs
• Write tool: Save search results for later reference