MCPサーバー

Wikexa Knowledge

com.wikexa/knowledge

このMCPでできること

Searches and retrieves structured information from Wikipedia, Wikidata, Wiktionary, and OpenAlex academic metadata.

article
The full text of an article, for when lookup()'s summary is not enough — sections as a JSON array, infobox as key/value facts, no HTML or wikitext to parse. Pass `sections` to pull only the parts you need (e.g. ["Early life"]) and `max_chars` to cap the payload; both exist because a long article will otherwise flood your context.
入力スキーマ
{'type': 'object', 'required': ['title'], 'properties': {'title': {'type': 'string', 'description': 'Article title, alias, or Q-id.'}, 'corpus': {'enum': ['wikipedia', 'wikiquote', 'wikibooks', 'wikivoyage', 'wikiversity'], 'type': 'string', 'description': 'Which corpus to read from. Defaults to wikipedia.'}, 'sections': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Optional section names to include (substring match, case-insensitive). Omit for the whole article.'}, 'max_chars': {'type': 'integer', 'description': 'Optional cap on total section text returned.'}}}
define
What a word means, in thousands of languages — 8.15M dictionary entries with senses, part of speech, etymology and pronunciation. Covers what a general model is weakest at: historical languages (Old English, Gothic, Ancient Greek, Middle French) and hundreds of regional and indigenous ones. A single spelling often has entries in many languages and you get all of them — `hund` returns Danish, Gothic, Icelandic, Middle English and more — or pass `language` to narrow, `pos` for one part of speech. Use this for words and lookup() for things: define("java") gives the word in eight languages, lookup("Java") gives the island.
入力スキーマ
{'type': 'object', 'required': ['word'], 'properties': {'pos': {'type': 'string', 'description': 'Optional part of speech filter, e.g. "Noun", "Verb", "Adjective".'}, 'word': {'type': 'string', 'description': 'The word or phrase to define.'}, 'language': {'type': 'string', 'description': 'Optional language name as Wiktionary spells it, e.g. "English", "Latin", "Spanish".'}}}
lookup
Facts about any named thing — person, company, place, species, event, concept. Returns structured fields (dates, identifiers, relationships) plus a ~200-token summary, drawn from 10.2M entity records. Prefer this over fetching an encyclopedia page: the HTML costs ~15,000 tokens to recover ~500 tokens of fact. Resolves aliases and Wikidata Q-ids, so "Apple", "Apple Inc" and "Q312" all reach the same entity. Free, no key.
入力スキーマ
{'type': 'object', 'required': ['entity'], 'properties': {'corpus': {'enum': ['wikipedia', 'wikiquote', 'wikibooks', 'wikivoyage', 'wikiversity'], 'type': 'string', 'description': 'Which corpus to look in. Defaults to wikipedia. Use wikivoyage for travel guides, wikiquote for quotations, wikibooks for textbooks, wikiversity for course material.'}, 'entity': {'type': 'string', 'description': 'Entity name, Wikipedia title, alias, or Wikidata Q-id (e.g. "Tim Cook", "Q312").'}}}
papers
Academic paper metadata from 27M+ works — title, abstract, authors, citations, DOI and open access URL. Covers every field: CS, medicine, physics, economics, biology, and more. Browse by OpenAlex topic ID and year, or filter by keywords in title/abstract. Returns papers sorted by citation count. Source: OpenAlex (CC0 metadata). Use this when the user needs scholarly references, citation counts, or research context that Wikipedia does not cover.
入力スキーマ
{'type': 'object', 'properties': {'year': {'type': 'integer', 'description': 'Publication year to filter on, e.g. 2023.'}, 'limit': {'type': 'integer', 'description': 'Maximum papers to return, 1-20 (default 5).'}, 'query': {'type': 'string', 'description': 'Keywords to match in title and abstract (all terms must appear). Combines with topic to narrow results.'}, 'topic': {'type': 'string', 'description': 'OpenAlex topic ID, e.g. "T10135" (Machine Learning), "T10461" (Quantum Computing). Required unless query is very specific.'}}}
recent
What changed in the last hours or days — the escape hatch for facts newer than your training cutoff. Reach for this whenever the answer could have moved since you were trained: elections, appointments, acquisitions, releases, deaths, records. Returns titles with timestamps and edit comments; resolve any of them with lookup(). Pass `topic` to filter and `hours` to widen the window up to a week.
入力スキーマ
{'type': 'object', 'properties': {'hours': {'type': 'integer', 'description': 'Look-back window in hours, 1-168 (default 24).'}, 'limit': {'type': 'integer', 'description': 'Maximum changes, 1-100 (default 25).'}, 'topic': {'type': 'string', 'description': 'Optional case-insensitive filter on title or edit comment.'}}}
search
Find the right title when you only have a partial name or a rough description. Returns ranked {title, wikidata_id, description, summary_snippet}; ranking blends text relevance with monthly pageviews and follows redirects, so abbreviations land on the real article — "usa" returns United States, "jfk" returns John F. Kennedy, "apple" returns Apple Inc. rather than a disambiguation page. Searches every corpus at once unless you pass `corpus`. Follow up with lookup() for facts or article() for the text.
入力スキーマ
{'type': 'object', 'required': ['query'], 'properties': {'limit': {'type': 'integer', 'description': 'Maximum results, 1-50 (default 10).'}, 'query': {'type': 'string', 'description': 'Free-text search query.'}, 'corpus': {'enum': ['wikipedia', 'wiktionary', 'wikiquote', 'wikibooks', 'wikivoyage', 'wikiversity'], 'type': 'string', 'description': 'Restrict to one corpus. Omit to search all of them at once, which is usually what you want when you do not know where the answer is.'}}}
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papers
2026年9月17日12:38
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recent
2026年9月17日12:38
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search
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define
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article
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lookup
2026年9月17日12:38