BioQC-MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@BioQC-MCPRun FastQC on all FASTQ files in ~/data and generate a MultiQC report."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
FastQC & MultiQC MCP Server
A professional Model Context Protocol (MCP) server for comprehensive bioinformatics quality control analysis. This server provides automated QC pipeline execution, HTML report analysis, and advanced data visualization for sequencing data.

๐ Quick Start
# 1. Clone and setup
git clone https://github.com/Babajan-B/BioQC-MCP.git
cd fastqc-multiqc-mcp-server
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# 2. Install prerequisites
brew install fastqc # macOS
pip install multiqc
# 3. Test the server
./tests/test_mcp_server.sh
# 4. Configure in Claude/Cursor (see below)Related MCP server: MCP Data Analytics Server
๐ Overview
This MCP server provides 10 specialized tools for bioinformatics quality control:
Tool | Description |
| Execute FastQC analysis on FASTQ files |
| Generate MultiQC aggregate reports |
| Auto-detect FASTQ files in directories |
| Extract quality metrics |
| Retrieve plot data |
| Read FastQC/MultiQC HTML reports |
| Parse HTML structure and data |
| Create custom visualizations (20+ chart types) |
| Combined extraction and visualization |
| ๐ Execute complete pipelines in a single call |
Key Capabilities:
Automated quality control workflows
HTML report interpretation
Advanced visualization (line, bar, scatter, heatmap, violin, box plots, etc.)
Publication-quality chart generation
Multi-sample analysis and aggregation
Code execution mode - 50-90% token savings for complex workflows
๐ฆ Installation
Prerequisites
Required:
Python 3.8+
FastQC
MultiQC
Install Commands:
# macOS
brew install fastqc
pip install multiqc
# Linux (Ubuntu/Debian)
sudo apt-get install fastqc
pip install multiqc
# Verify installation
fastqc --version
multiqc --versionSetup
# Clone repository
git clone https://github.com/Babajan-B/BioQC-MCP.git
cd fastqc-multiqc-mcp-server
# Create virtual environment
python3 -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install Python dependencies
pip install -r requirements.txt
# Verify setup
./tests/test_mcp_server.shโ๏ธ Configuration
Claude Desktop
Edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"fastqc-multiqc": {
"command": "/FULL/PATH/TO/venv/bin/python3",
"args": ["/FULL/PATH/TO/fastqc-multiqc-mcp-server/src/server.py"]
}
}
}Replace /FULL/PATH/TO/ with your actual installation path.
Restart Claude Desktop after saving.
Cursor IDE
Option 1: Quick Setup
# Copy example config
mkdir -p ~/Library/Application\ Support/Cursor/User/globalStorage
cp examples/cursor-mcp-config.json ~/Library/Application\ Support/Cursor/User/globalStorage/mcp.json
# Edit the file and update paths to your installation
# Then restart Cursor (โQ and reopen)Option 2: Manual Setup
Edit or create ~/Library/Application Support/Cursor/User/globalStorage/mcp.json:
{
"mcpServers": {
"fastqc-multiqc": {
"command": "/FULL/PATH/TO/venv/bin/python3",
"args": ["/FULL/PATH/TO/fastqc-multiqc-mcp-server/src/server.py"],
"env": {
"PATH": "/usr/local/bin:/opt/homebrew/bin:${PATH}",
"PYTHONUNBUFFERED": "1"
}
}
}
}Restart Cursor (โQ and reopen) after saving.
Verify: Open Cursor AI chat and ask: "What MCP tools are available?"
๐งช Testing
Automated Verification
# Run all checks
./tests/test_mcp_server.shThis verifies:
Python environment
Dependencies installed
FastQC/MultiQC available
Server syntax valid
Manual MCP Protocol Test
# Test MCP protocol compliance
python3 tests/test_server_manually.pyInteractive Testing with MCP Inspector
# Launch Inspector for interactive testing
./tests/launch_inspector.shNavigate to http://localhost:6274 and configure:
Command:
/FULL/PATH/TO/venv/bin/python3Arguments:
/FULL/PATH/TO/src/server.pyClick "Connect" to test tools interactively
๐ก Usage Examples
Quality Control Analysis
"Run FastQC analysis on sample1.fastq and sample2.fastq"
"Check the quality of all FASTQ files in ~/data/sequencing/"
"Create a MultiQC report for samples in ~/results/"Report Analysis
"Read the FastQC report at ~/results/sample_fastqc.html"
"What does the quality report say about adapter contamination?"
"Summarize the MultiQC report findings"Data Visualization
"Generate a line chart showing per-base quality scores"
"Create a bar chart comparing GC content across samples"
"Make a heatmap of quality metrics"Complete Workflow
"Analyze all FASTQ files in ~/data/, generate FastQC reports,
create a MultiQC summary, and show me a chart of overall quality scores"๐ Code Execution Pipeline (50-90% Token Savings)
Execute complete workflows in a single tool call using the run_qc_pipeline tool:
# AI writes and executes this pipeline:
files = list_fastq_files('/Users/jaan/Desktop/Alaa')
print(f"Found {len(files)} FASTQ files")
# Process files
for f in files:
result = run_fastqc([f['path']], output_dir='./qc_results')
print(f"Analyzed: {f['name']}")
# Generate aggregate report
multiqc = run_multiqc('./qc_results', output_dir='./report')
print(f"MultiQC report: {multiqc['report']}")
# Return structured result
result = {
"files_analyzed": len(files),
"report_path": multiqc['report']
}Available functions in pipeline:
list_fastq_files(directory)- Find FASTQ filesrun_fastqc(files, output_dir)- Execute FastQCrun_multiqc(input_dir, output_dir)- Generate MultiQCparse_fastqc_summary(fastqc_dir)- Extract metricsgenerate_chart(chart_type, data, title)- Create visualizations
Benefits (based on actual testing):
Metric | Traditional | Pipeline | Savings |
Token usage | 750 | 318 | 57.6% |
Tool calls | 4 | 1 | 75% |
Response time | 15s | 8s | 47% |
See skills/ directory for reusable pipeline templates.
๐ ๏ธ Troubleshooting
Tools Not Showing in Claude/Cursor
Verify paths in config file
# Check Python path which python3 # After activating venv # Check server path ls -la src/server.pyRe-run verification
./tests/test_mcp_server.shCheck logs
Claude: Check Developer console
Cursor: View > Developer > Toggle Developer Tools > Console
FastQC/MultiQC Not Found
# Verify installation
which fastqc
which multiqc
# If not found, install
brew install fastqc # macOS
pip install multiqc
# Check PATH in config
# Add to config JSON:
"env": {
"PATH": "/usr/local/bin:/opt/homebrew/bin:${PATH}"
}Server Won't Start
# Check dependencies
pip install -r requirements.txt
# Test server directly
source venv/bin/activate
python3 src/server.py
# Should show MCP protocol output
# Check syntax
python3 -m py_compile src/server.pyPermission Issues
# Make scripts executable
chmod +x tests/*.sh
chmod +x src/server.py๐ Project Structure
fastqc-multiqc-mcp-server/
โโโ src/
โ โโโ __init__.py
โ โโโ server.py # Main MCP server
โโโ tests/ # Testing utilities
โ โโโ test_mcp_server.sh # Automated verification
โ โโโ test_server_manually.py# MCP protocol test
โ โโโ test_real_fastqc.sh # Real data test
โ โโโ launch_inspector.sh # MCP Inspector launcher
โโโ examples/ # Configuration examples
โ โโโ cursor-mcp-config.json
โโโ docs/ # Additional documentation
โ โโโ TESTING_WITH_INSPECTOR.md
โ โโโ TESTING_COMPLETE.md
โ โโโ DEPLOYMENT_CHECKLIST.md
โ โโโ COMPARISON_BIOINFOMCP.md
โโโ requirements.txt # Python dependencies
โโโ CHANGELOG.md # Version history
โโโ LICENSE # MIT License
โโโ README.md # This file๐ง Technical Specifications
MCP Protocol: 2024-11-05
Python Version: 3.8+
Server Version: 2.0.0
Dependencies:
mcp >= 1.0.0 (Model Context Protocol)
pydantic >= 2.0.0 (data validation)
matplotlib >= 3.8.0 (visualization)
seaborn >= 0.13.0 (statistical graphics)
plotly >= 5.18.0 (interactive charts)
pandas >= 2.1.0 (data manipulation)
numpy >= 1.24.0 (numerical computing)
External Tools:
FastQC (quality control)
MultiQC (report aggregation)
Supported File Formats:
.fastq, .fq (uncompressed)
.fastq.gz, .fq.gz (gzip compressed)
๐ฏ Features
Quality Control Pipeline
โ Single and batch FASTQ analysis
โ Multi-sample aggregation
โ Automatic file discovery
โ Threaded execution support
โ All standard sequencing formats
Report Analysis
โ HTML report parsing
โ Structured data extraction
โ Quality metrics interpretation
โ Table and chart data extraction
โ No browser required
Visualization
โ 20+ chart types
โ Publication-quality output
โ Custom styling and themes
โ Multiple export formats
โ Interactive charts (Plotly)
๐ Tested & Verified
โ MCP Protocol 2024-11-05 compliant
โ Tested with FASTQ files
โ Claude Desktop integration (December 2025)
โ Cursor IDE ready
โ MCP Inspector validated
โ All 10 tools functional
โ Production ready
๐ Deployment
Share via GitHub
Create repository on GitHub
Push code:
git remote add origin https://github.com/Babajan-B/BioQC-MCP.git git branch -M main git push -u origin mainAdd topics:
mcp-server,bioinformatics,fastqc,quality-control
Users Install:
git clone https://github.com/Babajan-B/BioQC-MCP.git
cd fastqc-multiqc-mcp-server
./tests/test_mcp_server.sh # Verify setup
# Then configure in Claude/Cursor๐ License
MIT License - see LICENSE file for details.
๐ค Contributing
Contributions welcome! Please:
Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request
๐ง Support
Issues: GitHub Issues
Email: bioinformatics.bb@gmail.com
Documentation: See
docs/directory for additional guides
๐ Resources
Version: 2.0.0
Status: Production Ready
Last Updated: December 2025
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