"A query about Google Chrome or a related Chrome topic" matching MCP servers:

A Chrome extension-based Model Context Protocol server that enables AI assistants to control your browser, leveraging your existing configurations and login states for complex automation, content analysis, and semantic search.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

A Model Context Protocol server that enables AI assistants like Claude to read from, append to, and format text in Google Documents programmatically.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Enables agents to perform controlled enterprise data queries through semantic intent, with runtime validation of statistics, filters, granularity, permissions, and physical bindings. Exposes tools like semantic_query for safe, fail-closed access to data horizons and capabilities.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

A local MCP server that enables LLMs to securely search and read files within designated Google Drive folders. Supports Google Docs, Google Sheets, PDFs, and plain text with strict folder-scoping via service account authentication.

MCP server for querying your local Google Takeout activity (Search/YouTube history) via SQLite/FTS5. Provides tools for keyword search, activity stats, and database info, enabling AI assistants to cite your personal history.

A stdio MCP server that provides a single tool to query Gemini with Google Search grounding for current web information, returning synthesized answers with source links.

An MCP server that provides Google Search functionality with automatic API key rotation and intelligent quota management, enabling natural language search queries with advanced filtering options.

Fetches news articles from Google News, vectorizes them locally using ChromaDB, and enables semantic search through natural language queries.

A Model Context Protocol server that enables Claude to perform Google Custom Search operations by connecting to Google's search API.

Provides AI-powered search and documentation tools using Google Vertex AI or Gemini API with real-time web search grounding, enabling technical queries, code analysis, documentation retrieval, and architecture recommendations to overcome LLM knowledge gaps.