👤 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.

STDIO Mode (for MCP clients that require stdio)

# Run the server in stdio mode
uvx gget-mcp stdio

HTTP Mode (Web Server)

# Run the server in streamable HTTP mode on default (3002) port
uvx gget-mcp http

# Run on a specific port
uvx gget-mcp http --port 8000

SSE Mode (Server-Sent Events)

# Run the server in SSE mode
uvx gget-mcp sse

If you want to inspect the methods provided by the MCP server, use npx (you may need to install nodejs and npm):

For STDIO mode with uvx:

npx @modelcontextprotocol/inspector --config mcp-config.json --server gget-mcp

You can also run the inspector manually and configure it through the interface:

npx @modelcontextprotocol/inspector

After that you can explore the tools and resources with MCP Inspector at which is usually at 6274 port (note, if you run inspector several times it can change port)

Overview

This guide will walk you through setting up Claude Desktop with the gget_mcp extension and Google Drive integration on Windows. By the end, you'll be able to use Claude to fetch biological data (like gene sequences) and save them directly to your Google Drive folder with offline access.

Prerequisites

• Windows PC with administrator access
• Google account
• Stable internet connection

Step 1: Set Up Google Drive for Desktop with Offline Access

1. Download and install Google Drive for Desktop

2. Launch the application and sign in with your user account

3. Connect your project's shared Google account (if applicable)

4. Configure offline access for your working folder:

   • Navigate to the folder you want to work with in Google Drive
   • Right-click on the folder (e.g., "work" or your project folder)
   • Select "Available offline" from the context menu
   • This makes the folder accessible at a path like C:\GDrive\holy-bio-mcp\My Disk\work

   Important: This is different from just syncing - offline access ensures the files are locally available while still being part of your Google Drive structure.

Step 2: Install Claude Desktop

1. Download Claude Desktop for Windows
2. Run the installer and follow the setup wizard
3. Sign in with your Anthropic/Claude account
4. Complete the initial setup

Step 3: Install uv Package Manager