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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.
    MIT
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    The server integrates with the free IMF data API and provides various features to facilitate data retrieval and analysis. The server is built using the FastMCP framework and offers the following functionalities:
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    Apache 2.0
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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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    CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.
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    MIT
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    MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.
    27
    AGPL 3.0
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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%
    4
    AGPL 3.0
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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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    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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    An MCP server providing progressive discovery tools for SDMX statistical data, enabling AI agents to explore and access SDMX-compliant repositories through interactive tools, resources, and prompts.
    20
    MIT
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    MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.
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    MIT
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    Provides structured behavioral analysis grounded in Panksepp's affective neuroscience and pattern matching to understand human intent and behavior through primary emotional systems and transition dynamics.
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