Search MCP Servers

Embeddings, vector search, document storage, and full-text search with the open-source AI application database

Search the web using Kagi's search API

Interact & query with Meilisearch (Full-text & semantic search API)

Search engine for AI agents (search + extract) powered by Tavily

Search the bible reliably and repeatably ai-Bible Labs

Web search with Baidu Cloud's AI Search

(by hanchunglee) - Server implementation for Microsoft Bing Web Search API.

Provides Google Search results via the Google Custom Search API

Provides Google Vertex AI Search results by grounding a Gemini model with your own private data

Server that provides local document management and semantic search capabilities. Upload documents, search them with AI embeddings, and integrate seamlessly with MCP clients like Claude Desktop and vs code.

"primitive" RAG-like web search model context protocol (MCP) server that runs locally using Google's MediaPipe Text Embedder and DuckDuckGo Search.

An MCP server for Tavily's search & news API, with explicit site inclusions/exclusions

A Model Context Protocol (MCP) server implementation that provides AI models with access to Typesense search capabilities. This server enables LLMs to discover, search, and analyze data stored in Typesense collections.

A server that provides full web search, summaries and page extration for use with Local LLMs.

An MCP server that enables free web searching using Google search results, with no API keys required.

Production-ready RAG platform combining Graph RAG, vector search, and full-text search. Best choice for building your own Knowledge Graph and for Context Engineering

Web search using free multi-engine search (NO API KEYS REQUIRED) — Supports Bing, Baidu, DuckDuckGo, Brave, Exa, and CSDN.

A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously.

Web, Image, News, Video, and Local Point of Interest search capabilities using Brave's Search API

Local-first system capturing screen/audio with timestamped indexing, SQL/embedding storage, semantic search, LLM-powered history analysis, and event-triggered actions - enables building context-aware AI agents through a NextJS plugin ecosystem.