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

Related MCP server: Semantic Scholar MCP Server

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

1. Discover: search_wikipedia("quantum computing") → Find relevant articles

2. Overview: get_summary("Quantum computing") → Quick understanding

3. Deep dive: get_article("Quantum computing") → Full content when needed

4. 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:

1. Define the output model in models.py

2. Add the Wikipedia API method in wikipedia_client.py

3. 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

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests if applicable

5. 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! 🔍📚