Wikipedia MCP Server

A Model Context Protocol (MCP) server that provides Claude with real-time access to Wikipedia. This server implements 4 essential tools for effective Wikipedia research while demonstrating core MCP concepts.

Features

Core Tools

search_wikipedia - Find articles when you don't know exact titles
get_article - Retrieve full article content for detailed analysis
get_summary - Get concise summaries for quick understanding
find_related - Discover related articles to expand research

Key Benefits

🔄 Real-time data - Access current Wikipedia content, not training data
🏗️ Structured output - Well-defined schemas for reliable data access
🏠 Local control - Self-hosted, no API keys required
⚡ Fast & efficient - Async operations with proper error handling

Installation

Prerequisites

Python 3.9 or higher
uv (recommended) or pip

Install Dependencies

Using pip:

# Navigate to the project directory
cd wikipedia-mcp-server

# Install dependencies
pip install mcp httpx pydantic beautifulsoup4

Using uv (if available):

# Clone or create the project directory
cd wikipedia-mcp-server

# Install dependencies
uv sync

Usage

Running the Server

With python directly:

python -m src.wikipedia_mcp_server

With uv (if available):

uv run python -m src.wikipedia_mcp_server

Testing the Tools

You can test individual tools by running the server and connecting with an MCP client:

# Example: Search for articles
search_wikipedia("quantum computing", limit=3)

# Example: Get article summary
get_summary("Albert Einstein")

# Example: Get full article
get_article("Machine Learning")

# Example: Find related articles
find_related("Artificial Intelligence", limit=5)

Claude Desktop Integration

To use this MCP server with Claude Desktop, add it to your MCP configuration:

macOS/Linux Configuration

Edit ~/.config/claude-desktop/mcp.json:

{
  "mcpServers": {
    "wikipedia": {
      "command": "python",
      "args": [
        "-m", 
        "src.wikipedia_mcp_server"
      ],
      "cwd": "/ABSOLUTE/PATH/TO/wikipedia-mcp-server"
    }
  }
}

Windows Configuration

Edit %APPDATA%\Claude\mcp.json:

{
  "mcpServers": {
    "wikipedia": {
      "command": "python",
      "args": [
        "-m", 
        "src.wikipedia_mcp_server"
      ],
      "cwd": "C:\\ABSOLUTE\\PATH\\TO\\wikipedia-mcp-server"
    }
  }
}

Alternative: Using uv

If you have uv installed:

{
  "mcpServers": {
    "wikipedia": {
      "command": "uv",
      "args": [
        "run", 
        "python", 
        "-m", 
        "src.wikipedia_mcp_server"
      ],
      "cwd": "/ABSOLUTE/PATH/TO/wikipedia-mcp-server"
    }
  }
}

Research Workflows

Typical Research Flow

Discover: search_wikipedia("quantum computing") → Find relevant articles
Overview: get_summary("Quantum computing") → Quick understanding
Deep dive: get_article("Quantum computing") → Full content when needed
Expand: find_related("Quantum computing") → Related topics

Example Claude Conversation

User: I want to learn about Marie Curie's discoveries

Claude: I'll help you research Marie Curie's discoveries. Let me start by getting a summary of her Wikipedia article.

[Uses get_summary("Marie Curie")]

Based on the summary, Marie Curie was a pioneering scientist who discovered radium and polonium. Let me find related articles about her specific discoveries.

[Uses find_related("Marie Curie")]

Now let me get detailed information about her discovery of radium.

[Uses get_article("Radium")]

Tool Specifications

search_wikipedia

Purpose: Find articles when you don't know exact titles
Input: query (required), limit (1-10), language (default: "en")
Output: List of search results with titles, snippets, and URLs

get_article

Purpose: Retrieve full article content
Input: title (required), language (default: "en")
Output: Complete article with content, metadata, and sections

get_summary

Purpose: Get concise article summaries
Input: title (required), language (default: "en")
Output: Summary text with key facts

find_related

Purpose: Discover related articles
Input: title (required), limit (1-10), language (default: "en")
Output: Related articles with relationship types

Error Handling

The server includes comprehensive error handling for:

Invalid article titles
Network timeouts
Wikipedia API errors
Malformed requests
Rate limiting

Development

Project Structure

wikipedia-mcp-server/
├── src/wikipedia_mcp_server/
│   ├── __init__.py
│   ├── __main__.py
│   ├── server.py          # Main MCP server
│   ├── models.py          # Pydantic data models
│   └── wikipedia_client.py # Wikipedia API client
├── pyproject.toml
├── README.md
└── mvp.md

Key MCP Concepts Demonstrated

✅ Tool registration with @mcp.tool()
✅ Structured input/output with Pydantic models
✅ Async operations for performance
✅ Proper error handling and validation
✅ Clear tool documentation

Adding New Tools

To add a new tool:

Define the output model in models.py
Add the Wikipedia API method in wikipedia_client.py
Register the tool in server.py with @mcp.tool()

Performance Considerations

Async operations: All Wikipedia API calls are non-blocking
Response times: Typically < 3 seconds per operation
Rate limiting: Respects Wikipedia's guidelines
Error resilience: Graceful handling of API failures

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

License

This project is open source and available under the MIT License.

Acknowledgments

Built using the Model Context Protocol
Wikipedia data via the Wikipedia REST API
Uses the FastMCP framework

Happy researching with Claude and Wikipedia! 🔍📚

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

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.

Provides Claude with real-time access to Wikipedia through four essential tools: search articles, get full content, retrieve summaries, and find related articles. Enables comprehensive Wikipedia research workflows with structured data access and no API keys required.

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