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"Understanding Modal Music" matching MCP servers:

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    Enables deep probabilistic analysis of single-cell omics data using scvi-tools through natural language. Supports SCVI for scRNA-seq analysis, SCANVI for cell type annotation, TOTALVI for multi-modal RNA/protein data, and PEAKVI for scATAC-seq analysis.
    MIT
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    Enables formal logical reasoning, mathematical problem-solving, and proof construction across 11 logic systems including propositional, predicate, modal, fuzzy, and probabilistic logic. Integrates external solvers (Z3, ProbLog, Clingo) for advanced reasoning, with support for proof storage, argument scoring, and cross-system translation.
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    MIT
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    Turn any link — video, PDF, screenshot, or article — into cached, timestamp-anchored understanding agents can query through lenses (explainer, build spec, teardown, design tokens, production blueprint). Every claim carries the exact second or page it came from.
    MIT
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    Enables reading and understanding NetCDF files, providing tools to inspect structure, variables, metadata, and data quality for scientific datasets.
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    Provides AI assistants with structured, authoritative knowledge of the HICAR atmospheric model, including namelist options, physics schemes, output variables, documentation, and source code.
    24
    GPL 3.0
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    An MCP server that integrates AI retrievals with NASA's Common Metadata Repository (CMR), allowing users to search NASA's catalog of Earth science datasets through natural language queries.
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    An MCP server that provides a toolbox for interacting with EPA SWMM stormwater models, enabling users to analyze model data and interpret results through LLM-driven tools. It assists stormwater modelers in understanding hydraulic systems and modeling behavior using natural language interfaces.
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    MIT
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    A Python implementation of the Model Context Protocol (MCP) server that enables searching and extracting information from arXiv papers, designed to be extensible with additional MCP tools.