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    This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.
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    MIT
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    A self-contained MCP server that gives AI agents the ability to calculate high-precision astronomical data. It provides tropical zodiac coordinates, planetary speeds, retrograde detection, and house cusps using the trusted Swiss Ephemeris engine. 100%
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    AGPL 3.0
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    Enables simulation and analysis of M/M/1 and M/M/c queuing systems using SimPy, with tools for parameter validation, theoretical metric calculation, simulation execution, and comparison of separate vs pooled queue strategies.
    Last updated
    MIT
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    An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.
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    Enables discovery and analysis of research ecosystems by extracting metadata from paper URLs, GitHub repositories, and research names. Automatically finds related papers, code repositories, models, datasets, and authors across platforms like arXiv, HuggingFace, and GitHub.
    Last updated
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    Startup engineering acceleration signals for VC investors. Tracks commit velocity, contributor growth, and repo expansion across 20 sectors via public GitHub data. No API key required.
    Last updated
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    MIT
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    AI-powered code assistant that provides advanced search and discovery capabilities across GitHub and NPM ecosystems, helping users understand code patterns, implementations, and connections between repositories.
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    TypeScript
    MIT
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    Delivers comprehensive company intelligence in a single tool call, aggregating profiles, tech stacks, key people, news, and corporate data from any domain or company name. Sources data from Wikipedia, GitHub, OpenCorporates, and web scraping to provide structured business insights using only free public APIs.
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    MIT
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    For AI agents and humans: Discover researchers, publications, datasets, and code repositories across a federated network of researcher digital twins. Compute S-Index impact metrics combining citation data from Semantic Scholar and Google Scholar with code and dataset quality scores from GitHub and Figshare.
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    1
    MIT
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    Track how your brand appears in AI-generated answers across ChatGPT, Perplexity, and other AI models. Analyze visibility, sentiment, citations, and domain rankings with 31 tools — including analytics reports, chat inspection, query analysis, and full CRUD for brands, prompts, tags, and topics.
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    MIT