gget-mcp MCP Server

👤 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:

• Structured Access: Direct connection to powerful bioinformatics tools and databases
• Natural Language Queries: Simplified interaction with complex genomics workflows
• Type Safety: Strong typing and validation through FastMCP
• AI Integration: Seamless integration with AI assistants and agents

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.

Gene Information & Search

• gget_search: Find Ensembl IDs associated with search terms
• gget_info: Fetch detailed information for Ensembl IDs
• gget_seq: Retrieve nucleotide or amino acid sequences
• gget_ref: Get reference genome information from Ensembl

Sequence Analysis

• gget_blast: BLAST nucleotide or amino acid sequences
• gget_blat: Find genomic locations of sequences
• gget_muscle: Align multiple sequences

Expression & Functional Analysis

• gget_archs4: Get gene expression data from ARCHS4
• gget_enrichr: Perform gene set enrichment analysis

Protein Structure & Function

• gget_pdb: Fetch protein structure data from PDB
• gget_alphafold: Predict protein structure using AlphaFold

Cancer & Mutation Analysis

• gget_cosmic: Search COSMIC database for cancer mutations

Single-cell Analysis

• gget_cellxgene: Query single-cell RNA-seq data from CellxGene

# Download and install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Verify installation
uv --version
uvx --version

uvx is a very nice tool that can run a python package installing it if needed.