"Wikipedia" matching MCP servers:

Enables efficient Wikipedia access through a progressive retrieval strategy that minimizes token usage by searching, summarizing, and fetching only relevant sections.

A Model Context Protocol server that retrieves information from Wikipedia to provide context to LLMs, allowing users to search articles, get summaries, full content, sections, and links from Wikipedia.

A production-ready server that provides Wikipedia search and content retrieval tools through the Model Context Protocol, enabling AI assistants to search for articles, list sections, and retrieve specific content.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

An MCP server that enables interaction with Kiwix HTTP servers to browse, search, and retrieve content from offline ZIM books like Wikipedia. It provides tools for listing available library resources and fetching full-text articles as plain text.

A Model Context Protocol (MCP) server that coordinates AI agents to transform database tables into Wikipedia-style data dictionaries.

A high-performance MCP server that aggregates Wikipedia, arXiv, Context7, and DevDocs into a unified interface for AI agents.

OpenZIM MCP Server provides AI models structured, offline access to ZIM format knowledge archives (Wikipedia, Wiktionary, Stack Exchange, Kiwix Library) via a Model Context Protocol server with smart namespace navigation, full-text search with suggestions, structure-aware retrieval (sections, tables of contents, related articles), link-graph extraction, archive-type presets, and both Simple (single natural-language tool) and Advanced (8 specialized tools) modes with streamable HTTP transport, bearer auth, and per-entry resources with subscriptions.

An MCP server that provides AI agents with access to over 20 knowledge sources including arXiv, GitHub, YouTube, Wikipedia, and more for reading and searching content.

Enables agents to search and retrieve clean, structured content from local offline ZIM archives such as Wikipedia, medical docs, DevDocs, and Stack Exchange through a unified MCP interface.