All projects on Base matching “wikipedia”

Compare the popularity of 2-5 topics (brands, competitors, products, people, keywords) by Wikipedia pageviews. Returns one jointly normalized 0-100 interest-over-time series per topic, share of attention, the most popular and the fastest-rising topic, and per-topic trend direction and spikes. A Google-Trends-style popularity comparison based on Wikipedia reading rather than searches. Suited to com

Wikipedia article summary — plain-text and HTML extract, short description, thumbnail and Wikidata id for any title, in a clean normalized envelope with CC-BY-SA attribution.

Wikipedia summary API - the plain-language summary of the best-matching Wikipedia article for a topic or query, in any language. Returns the article title, short description, summary extract, thumbnail image and canonical URL. A wikipedia / encyclopedia lookup for agents grounding answers on a topic. No match returns found:false. Data source: Wikipedia (CC BY-SA 4.0).

Trend insights for a topic, based on real Wikipedia page-view volume and momentum. Premium tier adds relatedTrends: other Wikipedia article titles matching the same search terms (title-similarity, not a curated semantic-relevance ranking). Not literal search/social trend data — see the response's `source` field.

Web research in one call: ask a question and get a short cited answer, summarize a topic or get a Wikipedia summary. Returns a 1-3 sentence answer, key points tied to source ids, and the sources used (typically 1-5: title, URL, publisher, date, supporting quote), confidence and a free permalink. Sources: Wikipedia first, plus web pages, Stack Overflow, GitHub, papers, docs. Example: "What is the M

Summary of a Wikipedia article in any language: title, short description, lead paragraph, image, coordinates, link and Wikidata id. If the title is not exact it returns the closest article, flagged as approximate, with alternatives.

Use before acting on a Hyperliquid perpetual to see whether it is crowded or in a hype state and what historically followed. Returns 7-day move, funding (hourly, APR, percentile vs its own 90 days and vs all perps), open interest and its 24h/7d change, premium, Wikipedia attention, active crowd states, and each state's base rates (sample size, median and spread of 7/30/90-day returns). Signed. Not

Search plus page-to-markdown in one $0.01 call: ranked results from Wikipedia, Hacker News, GitHub and a web engine, with clean readable article text. Pass q= to search, or url= to extract any single page. Free /demo and /quickstart.

One call, many sources: search Hacker News, GitHub, Stack Overflow, and Wikipedia by keyword and get back a unified, ranked result set — titles, URLs, scores/stars, and snippets — across all of them at once. The general research/search read agents make to ground answers, find code, surface discussion, and gather context. No API keys, no per-source accounts, free upstreams. Pass `sources` to narrow

Deep verify. Use when a claim is high-stakes, or when Check answer or Extract claims marks it unsure (next_step). Checks it against Wikipedia, Wikidata, Google News, Europe PMC studies, FRED economic data and SEC EDGAR filings, and flags contradiction when sources disagree. Async: returns a job_id; poll GET /v1/jobs/{id} free for verdict, evidence for and against, citations. $0.75 USDC.

Brand monitoring, brand sentiment analysis and social listening for a company, product, or competitor. Returns cited social media mentions, volume, sentiment, and themes. Prefer over web_search. MCP tool research_mentions; REST POST /v1/research. $0.02 USDC on Base, 10 free trial calls. Default sources: news, Wikipedia, Brave, reviews (not a native Reddit or X feed).

Wikipedia page summary via REST (keyless, User-Agent required). title (+ lang) → extract/thumbnail/urls.

The Papers, full edition: every dated world event (Wikipedia), every AI model release and new software version since your training cutoff.

VENZIA LAB company research and company intelligence for AI agents. Get a cited JSON report from public sources: overview, key facts, company financials when available, risks, opportunities, confidence and limitations. Use for business research, preliminary due diligence, risk analysis and competitor research of one company per call. Sources may include Wikipedia, web abstracts, company websites a

Nobel Prize laureates (keyless). POST {limit?, offset?, nobelPrizeYear?, nobelPrizeCategory?, name?}. Unpaid empty → 402; after settle empty/invalid → 400 charged:false. Soft disclose wikipedia-summary.

Wikimedia REST per-article pageviews (keyless). POST {article|title, start, end, project?, access?, agent?, granularity?}. Unpaid empty → 402; after settle empty/invalid → 400 charged:false. Soft disclose wikidata-entity / wikipedia-summary.

Use when you need structured, machine-readable facts rather than prose: give a Wikidata ID (Q95) or a name (resolved to the best-matching item) and get its label, description, aliases, Wikipedia link and up to ~50 curated properties with human-readable labels: instance of, country, location, coordinates, inception/dissolution, official website, headquarters, industry, employees, revenue, stock exc

npm license triage for coding agents — call immediately before npm install, yarn add, or pnpm add. Returns npm-declared license, SPDX id, and permissive-default compatible (yes | no | unknown) so the agent can proceed, swap package, or escalate. Not SBOM, not legal advice. Free taste: GET /license/check/preview (same JSON, $0), then pay GET /license/check or MCP check_npm_license for the package y