minima for AI Search

MCP server delivering web and local search context

minima, developed by Dmayboroda, is an MCP server that supplies LLMs with searchable context for model responses. The app routes queries to web search providers and local file indexes so models can access live internet results and private documents. Key components include support for Tavily and SearXNG, configurable search parameters, and a TypeScript architecture for extensions. Developers and researchers running MCP-compatible clients gain a single point to feed external and local context into models.

What tasks the tool enables for LLMs

The app acts as a bridge between AI clients and searchable data sources, converting model requests into web searches and local file queries. It supports real-time search provider integration and indexing of directories so a model can request external context or private documents. The app claims full compliance with the Model Context Protocol, which lets MCP-aware clients receive search results within their normal prompt-and-response flow.

How reliable the search results are for model context

Reliability depends on source quality and configuration. Search outputs come from configured providers such as Tavily or self-hosted engines, and from locally indexed files; the developer exposed parameters for search depth and result counts so users can tune relevance. The project states a lightweight design that aims for low latency, and self-hosted provider support reduces exposure of queries to third-party services.

How difficult setup and extension are for developers

Setup requires an MCP host and basic Node.js skills. The server runs as a Node.js application and installs via npm, requiring a host environment that supports MCP. External web searches need provider API keys. The codebase is TypeScript-based, which the developer designed for adding new search engines or data sources, so extending the connector set requires writing TypeScript modules and registering them with the server.

Practical choice for MCP adopters, with a community-tested reference implementation

The project is well regarded among MCP early adopters and functions as a reference implementation for injecting search-derived context into models. Inspect the source on GitHub before integrating, since the repository and MIT license let teams review behavior. Use the app to augment model inputs, and plan independent checks of model outputs because external search results supplement but do not guarantee factual correctness.