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Wikipedia MCP Server

by Rudra-ravi
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Python
MIT License
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remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Integrations

  • Provides tools for searching Wikipedia articles, retrieving article summaries, getting full article content, extracting article sections, and finding links within articles.

Wikipedia MCP Server

A Model Context Protocol (MCP) server that retrieves information from Wikipedia to provide context to Large Language Models (LLMs). This tool helps AI assistants access factual information from Wikipedia to ground their responses in reliable sources.

Overview

The Wikipedia MCP server provides real-time access to Wikipedia information through a standardized Model Context Protocol interface. This allows LLMs to retrieve accurate and up-to-date information directly from Wikipedia to enhance their responses.

Features

  • Search Wikipedia: Find articles matching specific queries
  • Retrieve Article Content: Get full article text with all information
  • Article Summaries: Get concise summaries of articles
  • Section Extraction: Retrieve specific sections from articles
  • Link Discovery: Find links within articles to related topics
  • Related Topics: Discover topics related to a specific article
  • Multi-language Support: Access Wikipedia in different languages

Installation

Installing via Smithery

To install wikipedia-mcp for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @Rudra-ravi/wikipedia-mcp --client claude
# Install pipx if you don't have it sudo apt install pipx pipx ensurepath # Install the Wikipedia MCP server pipx install git+https://github.com/rudra-ravi/wikipedia-mcp.git

Using a virtual environment

# Create a virtual environment python3 -m venv venv # Activate the virtual environment source venv/bin/activate # Install the package pip install git+https://github.com/rudra-ravi/wikipedia-mcp.git

From source

# Clone the repository git clone https://github.com/rudra-ravi/wikipedia-mcp.git cd wikipedia-mcp # Create a virtual environment python3 -m venv wikipedia-mcp-env source wikipedia-mcp-env/bin/activate # Install in development mode pip install -e .

Usage

Running the server

# If installed with pipx wikipedia-mcp # If installed in a virtual environment source venv/bin/activate wikipedia-mcp # Specify transport protocol (default: stdio) wikipedia-mcp --transport stdio # For Claude Desktop wikipedia-mcp --transport sse # For HTTP streaming

Configuration for Claude Desktop

Add the following to your Claude Desktop configuration file:

{ "mcpServers": { "wikipedia": { "command": "wikipedia-mcp" } } }

Location of the configuration file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%/Claude/claude_desktop_config.json
  • Linux: ~/.config/Claude/claude_desktop_config.json

Available MCP Tools

The Wikipedia MCP server provides the following tools for LLMs to interact with Wikipedia:

search_wikipedia

Search Wikipedia for articles matching a query.

Parameters:

  • query (string): The search term
  • limit (integer, optional): Maximum number of results to return (default: 10)

Returns:

  • A list of search results with titles, snippets, and metadata

get_article

Get the full content of a Wikipedia article.

Parameters:

  • title (string): The title of the Wikipedia article

Returns:

  • Article content including text, summary, sections, links, and categories

get_summary

Get a concise summary of a Wikipedia article.

Parameters:

  • title (string): The title of the Wikipedia article

Returns:

  • A text summary of the article

get_sections

Get the sections of a Wikipedia article.

Parameters:

  • title (string): The title of the Wikipedia article

Returns:

  • A structured list of article sections with their content

Get the links contained within a Wikipedia article.

Parameters:

  • title (string): The title of the Wikipedia article

Returns:

  • A list of links to other Wikipedia articles

Get topics related to a Wikipedia article based on links and categories.

Parameters:

  • title (string): The title of the Wikipedia article
  • limit (integer, optional): Maximum number of related topics (default: 10)

Returns:

  • A list of related topics with relevance information

Example Prompts

Once the server is running and configured with Claude Desktop, you can use prompts like:

  • "Tell me about quantum computing using the Wikipedia information."
  • "Summarize the history of artificial intelligence based on Wikipedia."
  • "What does Wikipedia say about climate change?"
  • "Find Wikipedia articles related to machine learning."
  • "Get me the introduction section of the article on neural networks from Wikipedia."

MCP Resources

The server also provides MCP resources (similar to HTTP endpoints but for MCP):

  • search/{query}: Search Wikipedia for articles matching the query
  • article/{title}: Get the full content of a Wikipedia article
  • summary/{title}: Get a summary of a Wikipedia article
  • sections/{title}: Get the sections of a Wikipedia article
  • links/{title}: Get the links in a Wikipedia article

Development

Local Development Setup

# Clone the repository git clone https://github.com/rudra-ravi/wikipedia-mcp.git cd wikipedia-mcp # Create a virtual environment python3 -m venv venv source venv/bin/activate # Install the package in development mode pip install -e . # Install development dependencies pip install -r requirements.txt # Run the server wikipedia-mcp

Project Structure

  • wikipedia_mcp/: Main package
    • __main__.py: Entry point for the package
    • server.py: MCP server implementation
    • wikipedia_client.py: Wikipedia API client
    • api/: API implementation
    • core/: Core functionality
    • utils/: Utility functions

Troubleshooting

Common Issues

  • Connection Error: Ensure the command in claude_desktop_config.json is correct
  • Article Not Found: Check the exact spelling of article titles
  • Rate Limiting: Wikipedia API has rate limits; consider adding delays between requests
  • Large Articles: Some Wikipedia articles are very large and may exceed token limits

Understanding the Model Context Protocol (MCP)

The Model Context Protocol (MCP) is not a traditional HTTP API but a specialized protocol for communication between LLMs and external tools. Key characteristics:

  • Uses stdio (standard input/output) or SSE (Server-Sent Events) for communication
  • Designed specifically for AI model interaction
  • Provides standardized formats for tools, resources, and prompts
  • Integrates directly with Claude and other MCP-compatible AI systems

Claude Desktop acts as the MCP client, while this server provides the tools and resources that Claude can use to access Wikipedia information.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Connect with the Author

This server cannot be installed

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security - not tested
A
license - permissive license
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quality - not tested

A Model Context Protocol server that retrieves information from Wikipedia to provide context to LLMs, allowing users to search articles, get summaries, full content, sections, and links from Wikipedia.

  1. Overview
    1. Features
      1. Installation
        1. Installing via Smithery
        2. Using pipx (Recommended)
        3. Using a virtual environment
        4. From source
      2. Usage
        1. Running the server
        2. Configuration for Claude Desktop
      3. Available MCP Tools
        1. search_wikipedia
        2. get_article
        3. get_summary
        4. get_sections
        5. get_links
        6. get_related_topics
      4. Example Prompts
        1. MCP Resources
          1. Development
            1. Local Development Setup
            2. Project Structure
          2. Troubleshooting
            1. Common Issues
          3. Understanding the Model Context Protocol (MCP)
            1. Contributing
              1. License
                1. Connect with the Author
                  ID: c651o8f7gl