local-rag-search

What this skill does

# 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