Which integrations are available for this server?

Allows running the SAMtools MCP server within a [Docker](/mcp/servers/integrations/docker) container, providing isolated execution of genomic analysis tools and simplified deployment with volume mounting for BAM file processing. Enables installation of the SAMtools MCP through [Git](/mcp/servers/integrations/git) repository cloning, facilitating access to the source code for customization and development. Provides a [Python](/mcp/servers/integrations/python) interface for programmatically interacting with SAMtools functionality, allowing scripted operations on genomic data files.

SAMtools MCP (Model Control Protocol)

A Model Control Protocol implementation for SAMtools, providing a standardized interface for working with SAM/BAM/CRAM files.

Features

View and convert SAM/BAM/CRAM files
Sort alignment files
Index BAM/CRAM files
Generate statistics
Merge multiple BAM files
Calculate read depth
Index FASTA files
And more...

Core Capabilities

File Format Support: Handle SAM (text), BAM (binary), and CRAM (compressed) alignment files
Format Conversion: Convert between SAM, BAM, and CRAM formats seamlessly
Region-Specific Analysis: Extract and analyze specific genomic regions
Flag-Based Filtering: Filter reads based on SAM flags
Performance Optimization: Multi-threaded operations for sorting and merging
Statistical Analysis: Generate comprehensive alignment statistics

Tools Overview

Tool	Description	Key Features
`view`	View and convert alignment files	- Format conversion (SAM/BAM/CRAM) - Region filtering - Flag-based filtering - Header manipulation
`sort`	Sort alignment files	- Coordinate-based sorting - Name-based sorting - Memory per thread control - Multi-threading support
`index`	Index BAM/CRAM files	- BAI index generation - CSI index support - CRAM index creation
`merge`	Merge multiple BAM/CRAM files	- Multi-file merging - Thread-enabled processing - Header reconciliation
`depth`	Calculate read depth	- Per-base depth calculation - Region-specific analysis - Multi-file support
`flagstat`	Generate alignment statistics	- Comprehensive flag statistics - Quality checks - Paired-end metrics
`idxstats`	BAM/CRAM index statistics	- Reference sequence stats - Mapped/unmapped counts - Length information
`faidx`	Index FASTA files	- FASTA indexing - Region extraction - Sequence retrieval

Related MCP server: Nmap MCP Server

Installation

Using Docker (Recommended)

The easiest way to use SAMtools MCP is through Docker:

# Pull the Docker image docker pull nadhir/samtools-mcp:latest # Run the container docker run -it --rm nadhir/samtools-mcp:latest # To process BAM files, mount a volume: docker run -it --rm -v /path/to/your/bam/files:/data nadhir/samtools-mcp:latest

Local Installation

Clone the repository:

git clone https://github.com/your-username/samtools_mcp.git cd samtools_mcp

Install dependencies:

pip install uv uv pip install -r requirements.txt

Configuration

MCP Server Configuration

To configure the MCP server to use the Docker image, add the following to your MCP configuration file:

{ "servers": { "samtools": { "type": "docker", "image": "nadhir/samtools-mcp:latest", "volumes": [ { "source": "/path/to/your/data", "target": "/data" } ] } } }

Local MCP Configuration

To configure the MCP to run using uv, add the following to your ~/.cursor/mcp.json:

{ "samtools_mcp": { "command": "uv", "args": ["run", "--with", "fastmcp", "fastmcp", "run", "/path/to/samtools_mcp.py"] } }

Replace /path/to/samtools_mcp.py with the actual path to your samtools_mcp.py file.

Usage

Basic Commands

View BAM file:

from samtools_mcp import SamtoolsMCP mcp = SamtoolsMCP() result = mcp.view(input_file="/data/example.bam")

Sort BAM file:

result = mcp.sort(input_file="/data/example.bam", output_file="/data/sorted.bam")

Index BAM file:

result = mcp.index(input_file="/data/sorted.bam")

Advanced Usage

View specific region with flags:

result = mcp.view( input_file="/data/example.bam", region="chr1:1000-2000", flags_required="0x2", output_format="SAM" )

Sort by read name:

result = mcp.sort( input_file="/data/example.bam", output_file="/data/namesorted.bam", sort_by_name=True )

Calculate depth with multiple input files:

result = mcp.depth( input_files=["/data/sample1.bam", "/data/sample2.bam"], region="chr1:1-1000000" )

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

This server cannot be installed

-

security - not tested

F

license - not found

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

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