local-rag-search – OpenClaw Skill

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name: local-rag-search description: 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.

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

Local RAG Search - Agent Skill

An Agent Skill that teaches Claude how to effectively use the mcp-local-rag MCP server for intelligent web searches with semantic similarity ranking.

What This Skill Does

This skill enables agents to:

• Choose the right search tool based on the task (DuckDuckGo, Google, or multi-engine deep research)
• Formulate effective queries using natural language
• Tune parameters for different use cases (quick answers vs comprehensive research)
• Perform deep research across multiple search engines and topics
• Respect privacy by defaulting to DuckDuckGo

Prerequisites

This skill requires the mcp-local-rag MCP server to be installed and configured in your MCP client.

Install mcp-local-rag

Add to your MCP configuration:

{
  "mcpServers": {
    "mcp-local-rag": {
      "command": "uvx",
      "args": [
        "--python=3.10",
        "--from",
        "git+https://github.com/nkapila6/mcp-local-rag",
        "mcp-local-rag"
      ]
    }
  }
}

Or use Docker:

{
  "mcpServers": {
    "mcp-local-rag": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i", "--init",
        "-e", "DOCKER_CONTAINER=true",
        "ghcr.io/nkapila6/mcp-local-rag:v1.0.2"
      ]
    }
  }
}

Installation

Claude Desktop

1. Navigate to Settings → Skills
2. Click Add Skill → Add from folder
3. Select this skill folder (local-rag-search/)

Usage

Once both the MCP server and skill are loaded, simply ask Claude to search for information:

• "Search the web for the latest Python 3.13 features"
• "Do deep research on sustainable energy solutions"
• "Find technical documentation about Docker optimization"

Claude will automatically apply the skill's best practices to use the appropriate tools effectively.

Features

• ✅ Smart tool selection - Automatically chooses DuckDuckGo, Google, or deep research based on query
• ✅ Privacy-first - Defaults to DuckDuckGo for general searches
• ✅ Multi-engine research - Supports 9+ search backends for comprehensive coverage
• ✅ Semantic ranking - Uses RAG-like similarity scoring for most relevant results
• ✅ No external APIs - All processing runs locally with embedded models

Supported Search Backends

• DuckDuckGo (privacy-focused)
• Google (comprehensive)
• Bing, Brave, Yahoo, Yandex
• Wikipedia (factual/encyclopedia)
• Mojeek, Grokipedia

License

MIT - Same as the parent mcp-local-rag project