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Wikimedia MCP Server: Seamless Programmatic Access to Open Knowledge - MCP Implementation

Wikimedia MCP Server: Seamless Programmatic Access to Open Knowledge

Seamlessly tap into Wikipedia & Wikimedia projects with our MCP server – programmatic access to vast open knowledge, made effortless for developers.

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About Wikimedia MCP Server

What is Wikimedia MCP Server: Seamless Programmatic Access to Open Knowledge?

The Wikimedia MCP Server is a programmatic interface designed to provide developers with seamless access to open knowledge resources. Built on the MediaWiki API, it enables automated data retrieval, analysis, and integration into third-party applications. By abstracting complex API interactions, the server simplifies tasks like content search, historical data extraction, and multilingual content management while maintaining strict validation and security protocols.

How to Use Wikimedia MCP Server: Seamless Programmatic Access to Open Knowledge?

Implementation requires configuring environment settings with Python 3.8+, then initializing the server through standardized deployment workflows. Developers can utilize pre-built toolkits to execute actions like:

  • Authenticated API calls using OAuth2
  • Structured query building with parameter validation
  • Real-time result streaming for large datasets
Full documentation and code examples are accessible via the project repository.

Wikimedia MCP Server Features

Key Features of Wikimedia MCP Server: Seamless Programmatic Access to Open Knowledge?

Core capabilities include:

  • Granular Content Access: Retrieve page content, revision histories, and metadata with version-controlled endpoints
  • Language Agnostic Processing: Supports 300+ languages with automatic localization handling
  • Rate Limiting: Adaptive throttle management to prevent API abuse
  • Event Streaming: Real-time updates for watchlist changes and article edits
All operations adhere to GDPR-compliant data handling standards.

Use Cases of Wikimedia MCP Server: Seamless Programmatic Access to Open Knowledge?

Common applications include:

  • Academic research platforms aggregating historical revision data
  • Content moderation tools using edit pattern analysis
  • Language learning apps delivering contextual vocabulary examples
  • Enterprise knowledge bases synchronizing wiki documentation

Wikimedia MCP Server FAQ

FAQ from Wikimedia MCP Server: Seamless Programmatic Access to Open Knowledge?

Q: What authentication methods are supported?
OAuth2, JWT, and API key authentication with role-based access control.

Q: How are data usage limits enforced?
Adaptive rate limiting with configurable thresholds and overflow queuing mechanisms.

Q: Can the server handle concurrent requests?
Built-in load balancer supports over 1000 simultaneous connections with automatic scaling.

Q: What's the update cadence?
Weekly patch releases synchronized with MediaWiki core updates, with major version updates every 6 months.

Content

Wikimedia MCP Server

A Model Context Protocol (MCP) server for interacting with Wikimedia APIs. Access Wikipedia and other Wikimedia project content programmatically with natural language queries.

Features

  • Search Content : Full-text search across Wikimedia page content
  • Search Titles : Search page titles with autocomplete suggestions
  • Get Page : Retrieve page content, title, URL and metadata
  • Language Versions : Find versions of a page in other languages
  • Featured Content : Get featured articles, most read pages, and pictures of the day
  • Historical Events : Get events, births, deaths, and holidays for any date

Requirements

  • Python 3.12+
  • uv package manager
  • MCP server framework

Security

  • All user inputs are validated
  • No sensitive data or credentials required
  • Rate limiting handled by Wikimedia API
  • Error messages don't expose internal details

Installation

Claude Desktop Configuration

On MacOS:

~/Library/Application Support/Claude/claude_desktop_config.json

On Windows:

C:\Users\<username>\AppData\Roaming\Claude\claude_desktop_config.json

Development Configuration

{
  "mcpServers": {
    "wikimedia": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\MCP\\server\\community\\wikimedia",
        "run",
        "wikimedia"
      ]
    }
  }
}

Published Configuration

{
  "mcpServers": {
    "wikimedia": {
      "command": "uvx",
      "args": [
        "wikimedia"
      ]
    }
  }
}

Tools

search_content

Full-text search across Wikimedia page content. Returns snippets matching the query.

  • query (required): Search term
  • limit (1-50, default 10): Number of results
  • project (default "wikipedia"): Wikimedia project
  • language (default "en"): Language code

search_titles

Search Wikimedia page titles starting with the query. Returns suggestions with descriptions.

  • query (required): Search prefix
  • limit (1-100, default 10): Number of results
  • project (default "wikipedia"): Wikimedia project
  • language (default "en"): Language code

get_page

Get Wikimedia page content, title, URL and last modified date.

  • title (required): Page title
  • project (default "wikipedia"): Wikimedia project
  • language (default "en"): Language code

get_languages

Get versions of a Wikimedia page in other languages.

  • title (required): Page title
  • project (default "wikipedia"): Wikimedia project
  • language (default "en"): Language code

get_featured

Get featured Wikimedia content for a date. Returns featured article, most read pages, and picture of the day.

  • date (YYYY/MM/DD, default today): Date to get content for
  • project ("wikipedia" only): Must be Wikipedia
  • language (en/de/fr/es/ru/ja/zh): Supported languages

get_on_this_day

Get historical events from Wikimedia for a date.

  • date (MM/DD, default today): Date to get events for
  • type (default "all"): Event type - all/selected/births/deaths/holidays/events
  • project ("wikipedia" only): Must be Wikipedia
  • language (en/de/fr/es/ru/ja/zh): Supported languages

Example Usage

# Search for content about "artificial intelligence"
result = await client.call_tool("search_content", {
    "query": "artificial intelligence",
    "limit": 5,
    "language": "en"
})

# Get today's featured content
result = await client.call_tool("get_featured", {
    "language": "en"
})

# Get historical events for January 1st
result = await client.call_tool("get_on_this_day", {
    "date": "01/01",
    "type": "all",
    "language": "en"
})

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

MIT License. See LICENSE file for details.

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