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"author:musharna" matching MCP servers:

GET /v1/servers – MCP directory API reference
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    Renders LEGO LDraw model files (.ldr, .mpd, .dat) to high-quality images using headless Blender, enabling MCP clients to visually inspect LEGO builds.
    6
    135 PyPI
    3
    MIT
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    ▎ Provides 32 tools for plant-genomics locus lookup across 11 free public backends (Ensembl Plants, Phytozome, UniProtKB, Europe PMC, QuickGO, NCBI BLAST, Gramene, KEGG, STRING-DB, ATTED-II, BAR). Takes a TAIR-style locus plus optional organism and returns gene metadata, functional/pathway annotation, interactions, co-expression, and literature — in single-locus, batch, and cross-source synthesis.
    56
    156 PyPI
    7
    MIT
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    jobd is a self-hostable, GPU-aware job broker that queues and routes shell commands across your own machines — picking workers by available VRAM and tool tags, streaming logs back, and surviving across sessions. It ships native MCP integration, so agents can submit, monitor, and control long-running or GPU jobs as tools.
    9
    67 PyPI
    5
    MIT
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    Phylogenetic inference server driving IQ-TREE 2, always returning per-clade bootstrap support plus model selection, tree comparison, and alignment simulation, with detailed diagnostics like conflicting clades and model uncertainty.
    6
    91 PyPI
    GPL 2.0
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    Searches and fetches research datasets across Zenodo, DataCite (Dryad/Figshare/Dataverse/OSF), NCBI omics archives (GEO/SRA/BioProject), and the literature (PubMed/OpenAIRE) through one normalized model — deduplicating by DOI, expanding organism queries with NCBI Taxonomy synonyms, and bridging papers to the datasets they produced. Resolves citations and open-access full text, and downloads files.
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    2,407 PyPI
    4
    MIT
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    Enables plant phenotyping via MCP, returning plant trait measurements along with segmentation overlays to validate mask quality, preventing unreliable outputs.
    150 PyPI
    MIT
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    Enables interactive breeding program simulation and comparison via MCP, returning statistical distributions (mean, sd, CI) instead of single stochastic runs.
    122 PyPI
    GPL 3.0