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    An MCP server that gives LLMs access to Wikipedia, supporting search, article retrieval, summaries, sections, and related topics across multiple languages and language variants.
    22
    MIT
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    Enables AI agents to search, read, and explore Wikipedia articles via tools like summaries, categories, and random articles, with no API keys required.
    1
    MIT
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    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.
    22
    3,076 PyPI
    302
    MIT
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    Provides offline search and retrieval of Wikipedia articles using Kiwix .zim files, enabling LLMs to access full Wikipedia content without internet.
    3
    MIT
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    Enables an MCP client to run fully offline, natural-language searches over roughly 35,000 Wikipedia film synopses from 1901–2017, and to fetch individual film records filtered by title, year, genre or director. Tool results come back as citable Wikipedia sources, with no API key required.
    2
    MIT
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    AI-powered deep research agent with multi-source search (DuckDuckGo, Google News, Reddit, Wikipedia), structured analysis (SWOT, comparisons, timelines, Google Trends), financial data with charts, and PDF report generation. 11 MCP tools. Multi-LLM support (DeepSeek, Gemini, GLM, OpenAI).
    5
    2
    MIT
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    A Model Context Protocol server that enhances LLM capabilities by connecting to Wikipedia, internet search (Tavily), and financial data (Yahoo Finance) tools, enabling contextual responses to user queries.
    3
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    Enables offline CRUD and semantic search on Wikipedia ZIM archives via MCP tools for reading, writing, editing, deleting, and searching articles.
    1
    MIT
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    Enables AI models to retrieve high-quality context from Valyu's API, including search over Wikipedia, arXiv papers, and the web.
    19
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    Structured knowledge for AI agents — Wikipedia, Wikidata, Wiktionary and growing as clean JSON. 1.9M searchable. Free, no auth.
    MIT
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    Connects LLM clients to verified Ghanaian digital sources, enabling unified natural-language search across live news outlets, university research repositories, public service institution directories, and location-scoped Wikipedia context with hybrid neural and lexical reranking.
    MIT
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    Enables hybrid search (dense + sparse) over self-hosted indexes of continuously ingested public datasets (news, GitHub, Wikipedia, arXiv, small web, devdocs, Hacker News) using your own embedding model, served via the Model Context Protocol.
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    Enables agents to search and retrieve clean, structured content from local offline ZIM archives such as Wikipedia, medical docs, DevDocs, and Stack Exchange through a unified MCP interface.
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