MCP Wikipedia Knowledge
Data analysts and researchers: Search Wikipedia articles in any language with search_wikipedia, fetch summaries and sections via get_article_summary and get_article_sections, and query Wikidata entities with search_wikidata or custom SPARQL via query_wikidata_sparql. Uncover nearby places using search_wikipedia_nearby, featured content with get_wikipedia_featured, and related topics — no API key required.
Overview
Tap into Wikipedia's 60 million+ articles across 300+ languages and Wikidata's 100 million+ structured facts using 14 dedicated tools, all without API keys or restrictions—data flows directly from open-licensed sources. Search for articles with search_wikipedia, pull summaries via get_article_summary, or execute custom SPARQL queries on Wikidata using query_wikidata_sparql to uncover complex relationships like Nobel Prize winners in Physics. This server delivers verifiable, citable knowledge for AI applications needing reliable factual grounding.
Key Capabilities
Extract full article content in sections with get_article_sections, discover related topics via get_related_articles, and list media files including thumbnails and captions using get_article_media. For Wikidata, search entities by name with search_wikidata, retrieve structured properties like birth dates or occupations from get_wikidata_entity, and find entities by property—such as all US presidents with find_entities_by_property (P39, Q11696). Location-based discovery shines with search_wikipedia_nearby for articles near coordinates, while get_wikipedia_featured fetches daily trending articles, historical events, and images.
Use Cases
Build research assistants that cite Wikipedia sources by combining search_wikipedia results with get_article_summary extracts. Enrich datasets via Wikidata SPARQL queries, like listing South American countries (find_entities_by_property P30, Q18) or Picasso's works (P170, Q5593). Power location apps with search_wikipedia_nearby for nearby landmarks, multilingual bots using get_article_languages, or daily briefings from get_wikipedia_featured.
Who This Is For
Developers integrating factual knowledge into AI agents and RAG pipelines. Data analysts querying Wikidata graphs for entity resolution and taxonomy. Content creators and researchers needing multilingual, location-aware encyclopedia access with structured metadata.