Wikipedia, Wikidata and Wiktionary as clean JSON, not HTML. 1.9M searchable. Free, no auth.
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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.
Article title, alias, or Q-id.
Which corpus to read from. Defaults to wikipedia.
Optional section names to include (substring match, case-insensitive). Omit for the whole article.
Optional cap on total section text returned.
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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.
| Argument | Type | Description |
|---|---|---|
| title* | string | Article title, alias, or Q-id. |
| corpus | string | Which corpus to read from. Defaults to wikipedia. |
| sections | array | Optional section names to include (substring match, case-insensitive). Omit for the whole article. |
| max_chars | integer | Optional cap on total section text returned. |
{
"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."
}
}
}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.
| Argument | Type | Description |
|---|---|---|
| pos | string | Optional part of speech filter, e.g. "Noun", "Verb", "Adjective". |
| word* | string | The word or phrase to define. |
| language | string | Optional language name as Wiktionary spells it, e.g. "English", "Latin", "Spanish". |
{
"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\"."
}
}
}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.
| Argument | Type | Description |
|---|---|---|
| corpus | string | Which corpus to look in. Defaults to wikipedia. Use wikivoyage for travel guides, wikiquote for quotations, wikibooks for textbooks, wikiversity for course material. |
| entity* | string | Entity name, Wikipedia title, alias, or Wikidata Q-id (e.g. "Tim Cook", "Q312"). |
{
"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\")."
}
}
}Academic paper metadata from 57M+ works — title, abstract, authors, citations, DOI and open access URL. Covers every field: CS, medicine, physics, economics, biology, and more. Search by keywords (e.g. "transformer attention"), by topic name (e.g. "machine learning"), or by OpenAlex topic ID. 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.
| Argument | Type | Description |
|---|---|---|
| year | integer | Publication year to filter on, e.g. 2023. |
| limit | integer | Maximum papers to return, 1-20 (default 5). |
| query | string | Keywords to search for. Matches topic names to find the right research area, then filters by title and abstract. Example: "transformer attention", "CRISPR gene editing". |
| topic | string | Topic name or OpenAlex topic ID. Names are resolved automatically (e.g. "machine learning" → T10135). Use an ID like "T10135" if you already know it. Optional — query alone works. |
{
"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 search for. Matches topic names to find the right research area, then filters by title and abstract. Example: \"transformer attention\", \"CRISPR gene editing\"."
},
"topic": {
"type": "string",
"description": "Topic name or OpenAlex topic ID. Names are resolved automatically (e.g. \"machine learning\" → T10135). Use an ID like \"T10135\" if you already know it. Optional — query alone works."
}
}
}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.
| Argument | Type | Description |
|---|---|---|
| hours | integer | Look-back window in hours, 1-168 (default 24). |
| limit | integer | Maximum changes, 1-100 (default 25). |
| topic | string | Optional case-insensitive filter on title or edit comment. |
{
"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."
}
}
}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.
| Argument | Type | Description |
|---|---|---|
| limit | integer | Maximum results, 1-50 (default 10). |
| query* | string | Free-text search query. |
| corpus | string | 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. |
{
"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."
}
}
}