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wikipedia-mcp MCP Server

Rudra-ravi/wikipedia-mcp

Score: 59.5 ↓ 3.3% Rank #6487 MCP Server
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A Model Context Protocol (MCP) server that retrieves information from Wikipedia to provide context to LLMs.

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Overview

Rudra-ravi/wikipedia-mcp is a Python MCP server licensed under MIT. A Model Context Protocol (MCP) server that retrieves information from Wikipedia to provide context to LLMs. Topics: anthropic, llm, mcp-server, open-source, wikipedia, wikipedia-api, wikipedia-mcp-server, wikipedia-mcp.

Ranked #6487 out of 24846 indexed tools.

Has 10 contributors.

Ecosystem

Python MIT
anthropicllmmcp-serveropen-sourcewikipediawikipedia-apiwikipedia-mcp-serverwikipedia-mcp

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 299

299 stars → growing community

Freshness 25% 6mo ago

Last commit 6mo ago → stale

Issue Health 25% 89%

31/35 issues closed → responsive maintainer

Contributors 10% 10

10 contributors → active community

Dependents 25% 4

4 dependents → few downstream users

npm Downloads N/A
PyPI Downloads N/A
Forks 59
Description Good
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Freshness high impact

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Matched Queries

"mcp server""mcp-server""model context protocol""model-context-protocol"topic:mcp-server

From the README

# 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.

<a href="https://glama.ai/mcp/servers/@Rudra-ravi/wikipedia-mcp">
  
</a>

## 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 with enhanced diagnostics
- **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 topic
Read full README on GitHub →

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