MCP server

Wikexa Knowledge Official

com.wikexa/knowledge

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Wikipedia, Wikidata and Wiktionary as clean JSON, not HTML. 1.9M searchable. Free, no auth.

Website

At a glance

Type

MCP server

Publisher

wikexa.com (verified by the MCP Registry)

Status

Working at last check

Tools

6

Token cost

About 1,683 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http knowledge https://wikexa.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.knowledge]
url = "https://wikexa.com/mcp"

Gemini CLI

{"mcpServers":{"knowledge":{"httpUrl":"https://wikexa.com/mcp"}}}

Cursor

{"mcpServers":{"knowledge":{"url":"https://wikexa.com/mcp"}}}

VS Code

{"servers":{"knowledge":{"type":"http","url":"https://wikexa.com/mcp"}}}

Antigravity

agy mcp add knowledge https://wikexa.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"knowledge","server_url":"https://wikexa.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"knowledge","server_url":"https://wikexa.com/mcp"}

Tools

• article ~268 tokens
  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 wil
• define ~300 tokens
  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
• lookup ~270 tokens
  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 token
• papers ~339 tokens
  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. Return
• recent ~214 tokens
  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
• search ~292 tokens
  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"

Browse

• OpenAlex tools
• Research papers
• Health and medicine

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