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. 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.

**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.

## Best Practices

### Query Formulation 1. **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"

2. **Be specific**: Include context and details - Good: "React hooks best practices for 2024" - Better: "React useEffect cleanup function best practices"

### Tool Selection Strategy

1. **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 ) ```

2. **Technical/Scientific Query** → Use `rag_search_google` ``` rag_search_google( query="Docker multi-stage build optimization techniques", num_results=15, top_k=7 ) ```

3. **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 ) ```

4. **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

1. **Always cite sources**: When `include_urls=True`, reference the source URLs in your response 2. **Verify recency**: Check if the content appears current and relevant 3. **Cross-reference**: For important facts, use multiple search terms or engines 4. **Respect privacy**: Use DuckDuckGo for general queries unless specific needs require Google 5. **Batch related queries**: When researching a topic, create multiple related search terms for deep_research 6. **Semantic relevance**: Trust the RAG scoring - top results are semantically closest to the query 7. **Explain your choice**: Briefly mention which tool you're using and why

## Error Handling

If a search returns insufficient results: 1. Try rephrasing the query with different keywords 2. Switch to a different backend 3. Increase `num_results` parameter 4. 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

## 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