Local Rag Search

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.

Security Scan

The skill is an instruction-only agent helper that coherently teaches use of a local mcp-local-rag server for RAG-based web search; nothing requested by the skill (env vars, binaries, installs) is disproportionate to that purpose, though the docs contain a few small mismatches to be aware of.

Purpose & Capability

Name and description match the instructions: the skill is a guide for using an mcp-local-rag server to run RAG-style web searches across multiple backends. It does not request unrelated credentials or system access.

Instruction Scope

SKILL.md only instructs the agent to call mcp-local-rag tools (rag_search_*, deep_research) and to cite URLs, tune parameters, and switch backends. It does not ask the agent to read local secrets, arbitrary files, or unrelated config paths. Note: the docs claim 'no external APIs' / 'all processing runs locally' while also recommending Google/Bing backends—this is a documentation inconsistency (expected if the MCP server proxies or scrapes those engines, but the claim should be verified by inspecting the mcp-local-rag server).

Install Mechanism

The skill itself is instruction-only and has no install spec. The README shows optional installation of the external mcp-local-rag server via a git+pip-like command or a GHCR Docker image; those are external artifacts the user must opt into. The skill does not auto-download or execute code on install.

Credentials

No environment variables, credentials, or config paths are required by the skill. The README indicates the user must configure an MCP server entry in their MCP client, which is appropriate and proportional.

Persistence & Privilege

always:false and no special privileges requested. The skill is user-invocable and does not request permanent system presence or modifications to other skills' configs.

Assessment

This skill is an instruction-only helper that expects you to run an mcp-local-rag server (the README gives git and Docker options). Before installing or using it: (1) verify the mcp-local-rag repository and the GHCR Docker image (ghcr.io/nkapila6/mcp-local-rag:v1.0.2) are from a trusted source and review what network requests that server will make; (2) understand that using Google/Bing backends means queries/results will involve external search engines—the 'no external APIs' claim in the docs is inconsistent and should be validated by inspecting the server behavior; (3) be aware the skill will cause the agent to fetch web content and include URLs in responses, so avoid sending sensitive secrets in search queries; (4) if you deploy the recommended Docker image, check its Dockerfile and runtime environment for unexpected behavior. These checks will reduce risk even though the skill itself is internally coherent.

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