Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"gget-mcp": {
"args": [
"--from",
"gget-mcp@latest",
"stdio"
],
"command": "uvx"
}
}
}Are you the author?
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MCP (Model Context Protocol) server for the gget bioinformatics library.
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Checked can against OSV.dev.
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MCP (Model Context Protocol) server for the gget bioinformatics library.

This server implements the Model Context Protocol (MCP) for gget, providing a standardized interface for accessing powerful bioinformatics tools and databases. MCP enables AI assistants and agents to perform complex genomics queries through structured interfaces.
The gget bioinformatics toolkit provides efficient querying of genomic databases and includes functions for:
👤 User: "Analyze the TP53 gene - find its details and protein sequence, then check for cancer mutations"
🤖 AI Assistant: I'll help you analyze TP53 step by step:
[Uses gget_search] → Found TP53 (ENSG00000141510) on chromosome 17
[Uses gget_info] → TP53 is a protein-coding tumor suppressor gene
[Uses gget_seq with translate=True] → Retrieved 393 amino acid protein sequence
[Uses gget_cosmic] → Found hotspot mutations R175H, R248Q, R273H in multiple cancers
👤 User: "Now predict the 3D structure of this protein sequence: MSKGEELFTGVVPILVEL..."
🤖 AI Assistant: Let me analyze this sequence:
[Uses gget_blast] → Identified as Green Fluorescent Protein (GFP)
[Uses gget_alphafold] → Predicted classic beta-barrel structure with 90%+ confidence
👤 User: "Perform pathway enrichment for DNA repair genes: TP53, BRCA1, BRCA2, ATM"
🤖 AI Assistant: [Uses gget_enrichr] → Top pathways: DNA Damage Response, Homologous Recombination, Cell Cycle Checkpoints, p53 Signaling
See how powerful this is? You can now ask natural language questions about genes, sequences, mutations, and pathways - the AI does the complex bioinformatics work for you!
MCP is a protocol that bridges the gap between AI systems and specialized domain tools. It enables:
If you want to understand more about what the Model Context Protocol is and how to use it more efficiently, you can take the DeepLearning AI Course or search for MCP videos on YouTube.
gget_search: Find Ensembl IDs associated with search termsgget_info: Fetch detailed information for Ensembl IDsgget_seq: Retrieve nucleotide or amino acid sequencesgget_ref: Get reference genome information from Ensemblgget_blast: BLAST nucleotide or amino acid sequencesgget_blat: Find genomic location