wikexa

endpoint

https://wikexa.com/mcp

door code

d8e8710b7456a6b4

protocol

streamable-http ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is wikexa live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is wikexa free to use?

Yes — the hub reached it with no key and no payment.

What tools does wikexa have?

6 tools: article, define, search, lookup, recent, papers.

Is wikexa safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• article reads unknown never probed

  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.

  mcp-tool

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  {
    "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."
      }
    }
  }

  arguments 34 lines copy
• define reads unknown never probed

  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.

  mcp-tool

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  {
    "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\"."
      }
    }
  }

  arguments 20 lines copy
• search reads unknown 4h ago

  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.

  mcp-tool

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  {
    "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."
      }
    }
  }

  arguments 28 lines copy
• lookup reads unknown 4h ago

  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.

  mcp-tool

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  {
    "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\")."
      }
    }
  }

  arguments 23 lines copy
• recent reads unknown never probed

  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.

  mcp-tool

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  {
    "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."
      }
    }
  }

  arguments 17 lines copy
• papers reads unknown never probed

  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.

  mcp-tool

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  {
    "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."
      }
    }
  }

  arguments 21 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.