wikipedia-mcp MCP — 사용 사례, 설치 & 라이브 데모

왜 쓰나요

핵심 기능

10+ tools: search, article, summary, sections, links, coordinates, related topics
Multi-language with regional variants (zh-Hant, zh-Hans, sr-Latn, etc.)
Optional caching for repeat queries
Country/locale codes accepted alongside language codes
stdio, HTTP, streamable-HTTP transports

라이브 데모

실제 사용 모습

wikipedia.replay ▶ 준비됨

0/0

설치

클라이언트 선택

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "wikipedia": {
      "command": "uvx",
      "args": [
        "wikipedia-mcp"
      ],
      "_inferred": true
    }
  }
}

Claude Desktop → Settings → Developer → Edit Config 열기. 저장 후 앱 재시작.

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "wikipedia": {
      "command": "uvx",
      "args": [
        "wikipedia-mcp"
      ],
      "_inferred": true
    }
  }
}

Cursor는 Claude Desktop과 동일한 mcpServers 스키마 사용. 프로젝트 설정이 전역보다 우선.

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "wikipedia": {
      "command": "uvx",
      "args": [
        "wikipedia-mcp"
      ],
      "_inferred": true
    }
  }
}

Cline 사이드바의 MCP Servers 아이콘 클릭 후 "Edit Configuration" 선택.

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "wikipedia": {
      "command": "uvx",
      "args": [
        "wikipedia-mcp"
      ],
      "_inferred": true
    }
  }
}

Claude Desktop과 같은 형식. Windsurf 재시작 후 적용.

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "wikipedia",
      "command": "uvx",
      "args": [
        "wikipedia-mcp"
      ]
    }
  ]
}

Continue는 맵이 아닌 서버 오브젝트 배열 사용.

~/.config/zed/settings.json

{
  "context_servers": {
    "wikipedia": {
      "command": {
        "path": "uvx",
        "args": [
          "wikipedia-mcp"
        ]
      }
    }
  }
}

context_servers에 추가. 저장 시 Zed가 핫 리로드.

claude mcp add wikipedia -- uvx wikipedia-mcp

한 줄 명령. claude mcp list로 확인, claude mcp remove로 제거.

사용 사례

실전 활용법: wikipedia-mcp

Cite Wikipedia instead of relying on model memory

👤 Anyone doing research-style Q&A ⏱ ~2 min beginner

언제 쓸까: The model's training cutoff is older than the facts you need (new events, refreshed biographies, updated stats).

흐름

Ask with citation requirement

When did the 2026 Winter Olympics happen and where? Use the wikipedia MCP and cite the article.✓ 복사됨

→ Answer + Wikipedia article title/URL

결과: Answers with a verifiable source, less hallucination.

함정

Wikipedia itself has errors / vandalism — For high-stakes facts, cross-check with a second source

Build a one-page dossier on a person or topic

👤 Journalists, prep-for-meeting folks ⏱ ~10 min beginner

언제 쓸까: You're meeting someone unfamiliar or writing about a new-to-you topic and want structured context fast.

흐름

Summary first

Get a summary of [person/topic] from Wikipedia. Then list sections so I can pick which to deep-read.✓ 복사됨

→ Summary + TOC
Pull the key sections

Give me full text of the 'Career' and 'Controversies' sections.✓ 복사됨

→ Section text
Related threads

What related Wikipedia topics should I also skim?✓ 복사됨

→ Related topic list with 1-line summaries

결과: 10-minute dossier better than ad-hoc googling.

함정

Wikipedia page doesn't exist for the person — Fall back to web search (firecrawl) for less-covered subjects

함께 쓰기: notion

Compare how a topic is covered across languages

👤 Researchers, translators, international ops ⏱ ~15 min intermediate

언제 쓸까: You suspect the English and Chinese Wikipedia articles on a topic differ materially (they often do for geopolitical topics).

흐름

Pull both versions

Get Wikipedia articles on [topic] in English and Simplified Chinese (zh-Hans).✓ 복사됨

→ Two article bodies
Diff the framing

What facts are in one but not the other? How does each frame the controversy?✓ 복사됨

→ Structured comparison

결과: Nuanced multi-perspective understanding.

함정

Translation quality varies — Ask Claude to quote original language too for contested phrasings

조합

다른 MCP와 조합해 10배 효율

wikipedia + notion

Build a 'places visited' Notion DB with Wikipedia summaries

For each row in my 'Trips' Notion DB, fetch the Wikipedia summary of the destination city and populate the 'About' column.✓ 복사됨

wikipedia + google-map

Show a topic on a map

Get coordinates of these 10 battles from Wikipedia; render them on a static Google map.✓ 복사됨

도구

이 MCP가 노출하는 것

도구	입력	언제 호출	비용
search_wikipedia	query: str, language?: str, limit?: int	Find candidate articles	free
get_article	title: str, language?: str	Need full content; use summary if 2-3 sentences suffice	free
get_summary	title, language?	Quick context without full article cost	free
get_sections	title, language?	Decide which sections to pull before pulling full article	free
get_section	title, section, language?	Cheaper than full article when you know what you want	free
get_links	title, language?	Discover related articles	free
get_coordinates	title	Plot an article's place on a map	free
get_related_topics	title	Research-mode expansion	free

비용 및 제한

운영 비용

API 쿼터: Wikipedia rate limits: ~200 requests/sec from an IP; add delays for long scripts
호출당 토큰: Full articles 2k-15k tokens; summaries 200-500
금액: Free
팁: Always start with get_summary or get_sections — avoid pulling a whole get_article unless you need the full text.

보안

권한, 시크릿, 파급범위

자격 증명 저장: Optional Wikimedia OAuth token for higher rate limits

데이터 외부 송신: Outbound to Wikipedia's servers only

Wikipedia is crowd-edited — always treat as a starting point, not final source for contested facts.
Anonymous requests are throttled more aggressively; authenticate if you hit limits.

문제 해결

자주 발생하는 오류와 해결

403 Forbidden on heavy use

Anonymous rate limit hit. Provide a personal access token or slow down (add sleep).

Page not found

Titles are case-sensitive and disambiguation-specific. Use search_wikipedia first to get the canonical title.

Chinese article returns Traditional when I want Simplified

Pass language=zh-Hans or language=zh-Hant explicitly.

대안

wikipedia-mcp 다른 것과 비교

대안	언제 쓰나	단점/장점
DBpedia / Wikidata MCP	You want structured data (infobox fields) rather than prose	Better for querying facts; worse for reading
Direct Wikipedia API via fetch MCP	You need maximum control	You parse the JSON yourself

wikipedia-mcp