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Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.
Passed 13 of 19 test cases with the skill, and 9 of 19 without it.
and 3 more in the full report
6 fixed9 passed either way6 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When executing a quick single-topic search query for simple facts, what is the recommended value range for the initial results parameter `num_results`?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends setting `num_results` between 5 and 10. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
rag_search_ddgs - DuckDuckGo SearchUse this for privacy-focused, general web searches.
When to use:
Parameters:
query: Natural language search querynum_results: Initial results to fetch (default: 10)top_k: Most relevant results to return (default: 5)include_urls: Include source URLs (default: true)rag_search_google - Google SearchUse this for comprehensive, technical, or detailed searches.
When to use:
deep_research - Multi-Engine Deep ResearchUse this for comprehensive research across multiple search engines.
When to use:
Available backends:
duckduckgo: Privacy-focused general searchgoogle: Comprehensive technical resultsbing: Microsoft's search enginetest cases passed, vs 9 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.