SearXNG users tune settings for LLM search

The core issue is not that SearXNG is bad, it’s that it is doing exactly what a metasearch engine does: cast a wide net. For LLM workflows, that often means you need to bias hard toward precision over recall.

• –SearXNG’s official docs emphasize that it aggregates results from many search services and can be self-hosted, but the engine mix is configurable, so noisy defaults are expected in broad queries.
• –The search syntax supports selecting specific engines, categories, and languages, which is the first lever to pull when broad terms like “does” get interpreted too literally.
• –Official docs also show per-engine config, safe-search, locale, and category controls, so the right fix is usually a narrower instance profile rather than a generic “best settings” paste.
• –For MCP-style web search, this is a practical reminder that retrieval quality depends as much on search policy as on the model consuming it.