"Wikipedia" matching MCP servers:

Provides Wikipedia page view trend data including spike detection, historical traffic, and cross-platform comparison, enabling AI to access a leading indicator of public curiosity.

An MCP server that exposes Wikipedia page view trends as clean JSON, providing normalized 0-100 trend scores, growth rates over 3M/6M/12M/5Y windows, and live most-viewed article feeds, comparable across 15 sources including Google, YouTube, and TikTok.

Provides structured access to Wikipedia content including search, summaries, images, links, and more via MCP tools.

Delivers comprehensive company intelligence in a single tool call, aggregating profiles, tech stacks, key people, news, and corporate data from any domain or company name. Sources data from Wikipedia, GitHub, OpenCorporates, and web scraping to provide structured business insights using only free public APIs.

Search across a billion+ documents including academic papers, books, code, legal cases, forums, and Wikipedia. Provides search and detail tools for various content types.

Enables AI agents to search and fetch high-quality information from multiple sources, including general web APIs, Wikipedia, arXiv, Hacker News, Stack Exchange, and Crossref, with optional pro-mode deep research and clean markdown page extraction.

AI-powered deep research agent with multi-source search (DuckDuckGo, Google News, Reddit, Wikipedia), structured analysis (SWOT, comparisons, timelines, Google Trends), financial data with charts, and PDF report generation. 11 MCP tools. Multi-LLM support (DeepSeek, Gemini, GLM, OpenAI).

An MCP server that turns an AI agent into a slide author, enabling drafting decks in Pandoc Markdown, compiling to PDF or PowerPoint, validating with PNG exports, and pulling in research from the web, Wikipedia, and Semantic Scholar.

An MCP server that enables LLMs to search, summarize, and retrieve detailed information from Wikipedia across multiple languages. It supports automated fact-checking by allowing models to proactively verify factual claims using Wikipedia's database.

A multi-tool MCP server that enhances local LLMs with web search, document reading, scholarly research, Wikipedia access, and calculator functions. Provides comprehensive tools for information retrieval and computation without requiring API keys by default.

Combines YouTube subtitle extraction, Wikipedia concept lookup, and arXiv paper search into one pipeline without any API keys, enabling generation of comprehensive study notes from a single YouTube URL.

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

Provides 40 tools to Claude for Wikipedia citation analysis, edit history, and DOI/ISBN annotation, enabling citation quality scoring and metadata enrichment without leaving the chat.

Fetches Wikipedia page wikitext via Special:Export and parses wikitables into structured rows, surfacing machine-readable warnings for structurally broken or ambiguous tables.

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.

Real-time trend data from Google Trends (Search, Images, News, Shopping), YouTube, TikTok, Reddit, Amazon, Wikipedia, npm, Steam, Spotify, X (Twitter), App Store, Google Play, web traffic, and news sentiment via one MCP connection. Works with Claude, Cursor, VS Code, Windsurf, ChatGPT, and any MCP-compatible AI.

Enables AI models to retrieve high-quality context from Valyu's API, including search over Wikipedia, arXiv papers, and the web.

Enables fetching Wikipedia pageview statistics, including daily counts for specific articles, top articles for a day, and aggregate project views via the Wikimedia Pageviews API.

Enables autonomous research by integrating multiple free sources including web search, Wikipedia, arXiv, and Crossref, with no API keys required.

Enables AI agents to fact-check claims, verify citations, and check source freshness using Wikipedia, Wikidata, Crossref, and Wayback Machine.