What dependencies are required to run wikipedia-mcp-server?
Node.js and npm are required for the npx method; Docker is required for the Docker method.
@hatsu38
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"wikipedia": {
"command": "npx",
"args": [
"-y",
"wikipedia-mcp-server"
]
}
}
}Search Wikipedia articles by keyword(キーワードでWikipedia記事を検索する)
Get detailed information about a Wikipedia article(Wikipedia記事の詳細情報を取得する)
Get random Wikipedia articles(ランダムなWikipedia記事を取得する)
wikipedia-mcp-server is a Model Context Protocol (MCP) server that provides Wikipedia search and article retrieval tools for AI agents and MCP clients. It supports searching, getting article details, and fetching random articles from Wikipedia in multiple languages.
Add the server configuration to your MCP client (e.g., Cursor, Claude Desktop) using either npx or Docker. Three MCP tools are available: search_wikipedia, get_wikipedia_article, and get_random_wikipedia. Each tool accepts parameters such as query, title, language code, result limit, and content inclusion.
Node.js and npm are required for the npx method; Docker is required for the Docker method.
Any Wikipedia language code (e.g., "en", "ja", "fr") is supported. The default language is "ja" (Japanese).
All data is retrieved from the Wikipedia API in real time.
The server is subject to Wikipedia API changes and availability. Users should handle acquired data appropriately, especially when using with AI services.
No local installation is needed; run via npx (`npx -y wikipedia-mcp-server`) or Docker (`docker run -i --rm hatsu38/wikipedia-mcp-server`).
Painless E2E Automation for Mobile and Web

Request dedicated senior developers to work on your project, write job descriptions, and prep technical interviews — all without leaving your Claude chat. Just describe what you need, and Lemon.io MCP does the rest. 1–3

Connect your AI assistant to your own YOUB endurance training account. Read recorded activities with full analysis, planned sessions, race goals, wellness (HRV, sleep, resting HR) and your athletic profile. Create or mov
Hindsight: Agent Memory That Learns

DeepMark helps teachers deliver rapid, consistent marking with meaningful feedback for every student — in a fraction of the time. What once took a week, now takes one free period.
Comments