Wikipedia MCP

What is Wikipedia MCP?

Wikipedia MCP is a Model Context Protocol (MCP) server designed to provide Large Language Models (LLMs) with real-time access to information from Wikipedia. It acts as a bridge, allowing AI assistants to retrieve factual and up-to-date information directly from Wikipedia to enhance the accuracy and reliability of their responses.

How to use Wikipedia MCP?

Wikipedia MCP can be installed and run in several ways:

From PyPI (Recommended): pip install wikipedia-mcp
Via Smithery: npx -y @smithery/cli install @Rudra-ravi/wikipedia-mcp --client claude (for Claude Desktop)
Using pipx: pipx install git+https://github.com/rudra-ravi/wikipedia-mcp.git
Using a virtual environment: Standard Python virtual environment setup and pip install git+https://github.com/rudra-ravi/wikipedia-mcp.git
From source: Clone the repository and install in development mode.

After installation, the server can be run using the wikipedia-mcp command. It supports stdio (default for Claude Desktop) and sse transport protocols, and allows specifying the language (e.g., --language ja). For Claude Desktop integration, a specific JSON configuration needs to be added to the Claude Desktop configuration file.

Key Features of Wikipedia MCP

Search Wikipedia: Find articles matching specific queries.
Retrieve Article Content: Get the full text of Wikipedia articles.
Article Summaries: Obtain concise summaries of articles.
Section Extraction: Retrieve specific sections from articles.
Link Discovery: Find internal links within articles to related topics.
Related Topics: Discover topics related to a specific article.
Multi-language Support: Access Wikipedia in various languages.
Query-focused Summaries: Get summaries tailored to a specific query.
Section Summaries: Summarize individual sections of an article.
Key Fact Extraction: Extract key facts from articles, optionally focused on a specific topic.

Use Cases of Wikipedia MCP

Wikipedia MCP is primarily used to empower LLMs with accurate and current information from Wikipedia. Specific use cases include:

Grounding LLM Responses: Providing factual context to LLMs to prevent hallucinations and ensure reliable outputs.
AI Assistant Enhancement: Enabling AI assistants to answer user queries with information directly sourced from Wikipedia.
Information Retrieval for LLMs: Allowing LLMs to perform targeted searches and retrieve specific content from Wikipedia articles.
Contextual Understanding: Helping LLMs understand complex topics by providing access to detailed Wikipedia content, including sections, links, and related topics.

FAQ about Wikipedia MCP

Q: What is the Model Context Protocol (MCP)? A: MCP is a specialized protocol, not a traditional HTTP API, designed for communication between LLMs and external tools. It uses standard input/output (stdio) or Server-Sent Events (SSE) and provides standardized formats for tools and resources, integrating directly with AI systems like Claude.

Q: How do I configure Wikipedia MCP for Claude Desktop? A: You need to add a specific JSON configuration to your Claude Desktop configuration file, typically located in ~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%/Claude/claude_desktop_config.json on Windows, or ~/.config/Claude/claude_desktop_config.json on Linux.

Q: What are the common issues and troubleshooting tips? A: Common issues include connection errors (check command in configuration), article not found (verify spelling), rate limiting (consider adding delays), and large articles potentially exceeding token limits.

Q: Can I use Wikipedia MCP with other LLMs besides Claude? A: While the documentation highlights Claude Desktop integration, MCP is a standardized protocol, suggesting potential compatibility with other MCP-compatible AI systems.

Q: What kind of information can I retrieve from Wikipedia using this server? A: You can search for articles, retrieve full article content, get summaries, extract specific sections, find links, discover related topics, and even get query-focused summaries or extract key facts.

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.

Verified By

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

From PyPI (Recommended)

The easiest way to install the Wikipedia MCP server is directly from PyPI:

pip install wikipedia-mcp

This will install the latest stable version.

Installing via Smithery

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

npx -y @smithery/cli install @Rudra-ravi/wikipedia-mcp --client claude

Using pipx (Recommended)

# 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

# Specify language (default: en for English)
# wikipedia-mcp --language ja  # Example for Japanese

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_links`

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_related_topics`

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

`summarize_article_for_query`

Get a summary of a Wikipedia article tailored to a specific query.

Parameters:

title (string): The title of the Wikipedia article
query (string): The query to focus the summary on
max_length (integer, optional): Maximum length of the summary (default: 250)

Returns:

A dictionary containing the title, query, and the focused summary

`summarize_article_section`

Get a summary of a specific section of a Wikipedia article.

Parameters:

title (string): The title of the Wikipedia article
section_title (string): The title of the section to summarize
max_length (integer, optional): Maximum length of the summary (default: 150)

Returns:

A dictionary containing the title, section title, and the section summary

`extract_key_facts`

Extract key facts from a Wikipedia article, optionally focused on a specific topic within the article.

Parameters:

title (string): The title of the Wikipedia article
topic_within_article (string, optional): A specific topic within the article to focus fact extraction
count (integer, optional): Number of key facts to extract (default: 5)

Returns:

A dictionary containing the title, topic, and a list of extracted facts

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
summary/{title}/query/{query}/length/{max_length}: Get a query-focused summary of an article
summary/{title}/section/{section_title}/length/{max_length}: Get a summary of a specific article section
facts/{title}/topic/{topic_within_article}/count/{count}: Extract key facts from an 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 and test dependencies
pip install -r requirements-dev.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
tests/: Test suite
- test_basic.py: Basic package tests
- test_cli.py: Command-line interface tests
- test_server_tools.py: Comprehensive server and tool tests

Testing

The project includes a comprehensive test suite to ensure reliability and functionality.

Test Structure

The test suite is organized in the tests/ directory with the following test files:

test_basic.py: Basic package functionality tests
test_cli.py: Command-line interface and transport tests
test_server_tools.py: Comprehensive tests for all MCP tools and Wikipedia client functionality

Running Tests

Run All Tests

# Install test dependencies
pip install -r requirements-dev.txt

# Run all tests
python -m pytest tests/ -v

# Run tests with coverage
python -m pytest tests/ --cov=wikipedia_mcp --cov-report=html

Run Specific Test Categories

# Run only unit tests (excludes integration tests)
python -m pytest tests/ -v -m "not integration"

# Run only integration tests (requires internet connection)
python -m pytest tests/ -v -m "integration"

# Run specific test file
python -m pytest tests/test_server_tools.py -v

Test Categories

Unit Tests

WikipediaClient Tests: Mock-based tests for all client methods
- Search functionality
- Article retrieval
- Summary extraction
- Section parsing
- Link extraction
- Related topics discovery
Server Tests: MCP server creation and tool registration
CLI Tests: Command-line interface functionality

Integration Tests

Real API Tests: Tests that make actual calls to Wikipedia API
End-to-End Tests: Complete workflow testing

Test Configuration

The project uses pytest.ini for test configuration:

[pytest]
markers =
    integration: marks tests as integration tests (may require network access)
    slow: marks tests as slow running

testpaths = tests
addopts = -v --tb=short

Continuous Integration

All tests are designed to:

Run reliably in CI/CD environments
Handle network failures gracefully
Provide clear error messages
Cover edge cases and error conditions

Adding New Tests

When contributing new features:

Add unit tests for new functionality
Include both success and failure scenarios
Mock external dependencies (Wikipedia API)
Add integration tests for end-to-end validation
Follow existing test patterns and naming conventions

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

🌐 Portfolio: ravikumar-dev.me
📝 Blog: Medium
💼 LinkedIn: in/ravi-kumar-e
🐦 Twitter: @Ravikumar_d3v

Wikipedia MCP Overview

What is Wikipedia MCP?

How to use Wikipedia MCP?

Key Features of Wikipedia MCP

Use Cases of Wikipedia MCP

FAQ about Wikipedia MCP

Wikipedia MCP's README

Wikipedia MCP Server

Overview

Verified By

Features

Installation

From PyPI (Recommended)

Installing via Smithery

Using pipx (Recommended)

Using a virtual environment

From source

Usage

Running the server

Configuration for Claude Desktop

Available MCP Tools

search_wikipedia

get_article

get_summary

get_sections

get_links

get_related_topics

summarize_article_for_query

summarize_article_section

extract_key_facts

Example Prompts

MCP Resources

Development

Local Development Setup

Project Structure

Testing

Test Structure

Running Tests

Run All Tests

Run Specific Test Categories

Test Categories

Unit Tests

Integration Tests

Test Configuration

Continuous Integration

Adding New Tests

Troubleshooting

Common Issues

Understanding the Model Context Protocol (MCP)

Contributing

License

Connect with the Author

Wikipedia MCP Reviews

Actions

Wikipedia MCP's Information

`search_wikipedia`

`get_article`

`get_summary`

`get_sections`

`get_links`

`get_related_topics`

`summarize_article_for_query`

`summarize_article_section`

`extract_key_facts`