Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of RAG-based web search with DuckDuckGo, Google, and multi-engine deep research capabilities.
This skill enables you to effectively use the mcp-local-rag MCP server for intelligent web searches with semantic ranking. The server performs RAG-like similarity scoring to prioritize the most relevant results without requiring any external APIs.
Available Tools
1. rag_search_ddgs - DuckDuckGo Search
Use this for privacy-focused, general web searches.
When to use:
User prefers privacy-focused searches
General information lookup
Default choice for most queries
Parameters:
query: Natural language search query
num_results: Initial results to fetch (default: 10)
top_k: Most relevant results to return (default: 5)
include_urls: Include source URLs (default: true)
2. rag_search_google - Google Search
Use this for comprehensive, technical, or detailed searches.
When to use:
Technical or scientific queries
Need comprehensive coverage
Searching for specific documentation
3. deep_research - Multi-Engine Deep Research
Use this for comprehensive research across multiple search engines.
Factual/Encyclopedia Content → Use deep_research with Wikipedia
deep_research(
search_terms=["World War II timeline", "WWII key battles"],
backends=["wikipedia"],
num_results_per_term=5
)
Parameter Tuning
For quick answers:
num_results=5-10, top_k=3-5
For comprehensive research:
num_results=15-20, top_k=7-10
For deep research:
num_results_per_term=10-15, top_k_per_term=3-5
Use 2-5 related search terms
Use 1-3 backends (more = more comprehensive but slower)
Workflow Examples
Example 1: Current Events
Task: "What happened at the UN climate summit last week?"
1. Use rag_search_google for recent news coverage
2. Set top_k=7 for comprehensive view
3. Present findings with source URLs
Example 2: Technical Deep Dive
Task: "How do I optimize PostgreSQL queries?"
1. Use deep_research with multiple specific terms:
- "PostgreSQL query optimization techniques"
- "PostgreSQL index best practices"
- "PostgreSQL EXPLAIN ANALYZE tutorial"
2. Use backends=["google", "stackoverflow"] if available
3. Synthesize findings into actionable guide
Example 3: Multi-Perspective Research
Task: "Research the impact of remote work on productivity"
1. Use deep_research with diverse search terms:
- "remote work productivity statistics 2024"
- "hybrid work model effectiveness studies"
- "work from home challenges research"
2. Use backends=["google", "duckduckgo"] for broad coverage
3. Synthesize different perspectives and studies
Guidelines
Always cite sources: When include_urls=True, reference the source URLs in your response
Verify recency: Check if the content appears current and relevant
Cross-reference: For important facts, use multiple search terms or engines
Respect privacy: Use DuckDuckGo for general queries unless specific needs require Google
Batch related queries: When researching a topic, create multiple related search terms for deep_research
Semantic relevance: Trust the RAG scoring - top results are semantically closest to the query
Explain your choice: Briefly mention which tool you're using and why
Error Handling
If a search returns insufficient results:
Try rephrasing the query with different keywords
Switch to a different backend
Increase num_results parameter
Use deep_research with multiple related search terms
Privacy Considerations
DuckDuckGo: Privacy-focused, doesn't track users
Google: Most comprehensive but tracks searches
Recommend DuckDuckGo as default unless user specifically needs Google's coverage
