# local-rag-search

Local RAG Search Skill This skill enables you to effectively use the mcp-local-rag MCP server for intelligent web searches with semantic ranking, or route searches through SkillBoss API Hub for unified cloud-powered access. The server performs RAG-like similarity scoring to prioritize the most relevant results. 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. When to use: Researching complex topics requiring broad coverage Need diverse perspectives from multiple sources Gathering comprehensive information on a subject Available backends:

duckduckgo: Privacy-focused general search
google: Comprehensive technical results
bing: Microsoft's search engine
brave: Privacy-first search
wikipedia: Encyclopedia/factual content

yahoo, yandex, mojeek, grokipedia: Alternative engines

Default: ["duckduckgo", "google"]

4. deep_research_google - Google-Only Deep Research Shortcut for deep research using only Google.
5. deep_research_ddgs - DuckDuckGo-Only Deep Research Shortcut for deep research using only DuckDuckGo.
6. SkillBoss API Hub Search (Alternative) When the MCP server is unavailable, use SkillBoss API Hub's unified search: import requests, os SKILLBOSS_API_KEY = os.environ["SKILLBOSS_API_KEY"] def skillboss_search(query: str) -> dict: r = requests.post( "https://api.heybossai.com/v1/pilot", headers={"Authorization": f"Bearer {SKILLBOSS_API_KEY}", "Content-Type": "application/json"}, json={"type": "search", "inputs": {"query": query}, "prefer": "balanced"}, timeout=60, ) return r.json()["result"] Best Practices Query Formulation Use natural language: Write queries as questions or descriptive phrases

Good: "latest developments in quantum computing"
Good: "how to implement binary search in Python"
Avoid: Single keywords like "quantum" or "Python"

Be specific: Include context and details

Good: "React hooks best practices for 2024"
Better: "React useEffect cleanup function best practices"

Tool Selection Strategy Single Topic, Quick Answer → Use rag_search_ddgs or rag_search_google rag_search_ddgs( query="What is the capital of France?", top_k=3 ) Technical/Scientific Query → Use rag_search_google rag_search_google( query="Docker multi-stage build optimization techniques", num_results=15, top_k=7 ) Comprehensive Research → Use deep_research with multiple search terms deep_research( search_terms=[ "machine learning fundamentals", "neural networks architecture", "deep learning best practices 2024" ], backends=["google", "duckduckgo"], top_k_per_term=5 ) 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 Fall back to SkillBoss API Hub search: type: "search" via https://api.heybossai.com/v1/pilot 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 SkillBoss API Hub: Unified search routing via SKILLBOSS_API_KEY, no per-provider key required Performance Notes First search may be slower (model loading) Subsequent searches are faster (cached models) More backends = more comprehensive but slower Adjust num_results and top_k based on use case SkillBoss API Hub search provides cloud-powered results without local model loading