I’ve been experimenting with web search in Open WebUI using smaller local models like Qwen3.5 4B and 9B.

The main problem was/is was context churn..

A typical search can dump partially relevant pages, navigation, repeated boilerplate, duplicate information, and huge chunks containing only one useful paragraph into the model.

That makes local setups slower and forces smaller models to spend their limited context window filtering noise instead of reasoning.

So I built TinySearch, an open-source, local-first MCP server that does most of the retrieval work before anything reaches the LLM:

\* searches and ranks results  
\* crawls the strongest pages  
\* extracts and chunks readable content  
\* deduplicates and reranks passages  
\* returns a compact evidence packet with source URLs

The goal is simple: less context churn, faster local web research, and more tokens spent on reasoning over actual evidence.

TinySearch works with self-hosted SearXNG, and Open WebUI can connect to it over HTTP MCP.

GitHub:    
\[https://github.com/MarcellM01/TinySearch\](https://github.com/MarcellM01/TinySearch)

Documentation:  
\[https://tinysuite.dev/docs/tinysearch\](https://tinysuite.dev/docs/tinysearch)

I’m the builder, so obvious bias, but I’d love feedback from Open WebUI users, especially anyone running smaller Qwen, Gemma, Llama, or Mistral models.

Does this solve a real bottleneck in your setup, or is native agentic search already enough?

I built a local web-research MCP that filters pages before Open WebUI sends them to the model