biotools-mcp-server

BACH-AI-Tools/biotools-mcp-server

3.2

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Biotools MCP Server is a comprehensive Model Context Protocol server designed for bioinformatics research, providing AI applications with access to major biological databases and analysis tools.

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Biotools MCP Server

A comprehensive Model Context Protocol (MCP) server for bioinformatics research, providing AI applications with access to major biological databases and analysis tools including PubMed, UniProt, NCBI GenBank, KEGG, PDB, and more.

🧬 Available Tools (37 Total)

📚 Literature Research Tools (3 tools)

search_pubmed

Search PubMed for scientific publications with advanced filtering capabilities.

  • Purpose: Find relevant research papers and studies
  • Input: Search terms (e.g., "CRISPR gene editing", "cancer genomics")
  • Returns: Publication metadata, abstracts, authors, and bibliographic information
get_publication_details

Retrieve comprehensive details for a specific publication by PMID.

  • Purpose: Get complete bibliographic record with all metadata
  • Input: PubMed ID (PMID)
  • Returns: Full MEDLINE record including authors, affiliations, funding sources, MeSH terms, citations, and publication history
get_publication_abstract

Extract the full abstract for a specific publication.

  • Purpose: Get structured abstract content with section labels
  • Input: PubMed ID (PMID)
  • Returns: Complete abstract text with metadata

🧬 Protein Analysis Tools (3 tools)

search_uniprot

Search the UniProtKB database for proteins with comprehensive field extraction.

  • Purpose: Find proteins by name, function, organism, or other criteria
  • Input: Search query (e.g., "insulin", "kinase AND human", "P04637")
  • Returns: Protein entries with 90+ comprehensive fields including function, domains, and cross-references
get_protein_entry

Get detailed information for a specific protein by UniProt accession.

  • Purpose: Retrieve complete protein annotation and functional data
  • Input: UniProt accession number (e.g., "P38398")
  • Returns: Exhaustive protein data including domains, PTMs, variants, tissue specificity, disease associations, and cross-references to 80+ databases
get_protein_sequence

Retrieve protein sequence in FASTA format with structural context.

  • Purpose: Get amino acid sequence with feature annotations
  • Input: UniProt accession number
  • Returns: FASTA sequence with complete metadata and structural features

🧬 Nucleotide Sequence Analysis Tools (4 tools)

get_nucleotide_sequence

Retrieve nucleotide sequences from GenBank, RefSeq, or Ensembl databases.

  • Purpose: Get DNA/RNA sequences with complete annotation
  • Input: Accession number (e.g., "NM_000546") and database preference
  • Returns: Complete sequence records with features, references, and comprehensive annotation
compare_annotations

Compare genomic annotations between prokaryotic and eukaryotic sequences.

  • Purpose: Analyze differences in gene structure and annotation between organism types
  • Input: Two sequence identifiers for comparison
  • Returns: Detailed comparison of features, similarities, and differences with biological insights
find_intron_exons

Detect intron-exon boundaries in gene sequences with splice site analysis.

  • Purpose: Analyze gene structure and identify coding regions
  • Input: Gene sequence identifier with optional organism context
  • Returns: Complete gene structure with exons, introns, splice sites, and coding sequence analysis
align_promoters

Align promoter regions from multiple genes to discover conserved regulatory elements.

  • Purpose: Find conserved motifs and regulatory sequences in gene promoters
  • Input: List of 2-10 gene identifiers
  • Returns: Promoter alignment with conserved elements and regulatory motif analysis

🧪 Enhanced Protein Analysis Tools (3 tools)

get_cross_references

Get comprehensive cross-references for a protein from multiple databases.

  • Purpose: Find related information across KEGG, Pfam, PDB, InterPro, GO, and other databases
  • Input: UniProt accession number
  • Returns: Complete cross-references from 80+ databases including structures, domains, pathways, and functional annotations
analyze_ptms

Analyze post-translational modifications with functional impact assessment.

  • Purpose: Identify and analyze protein modifications and their biological significance
  • Input: UniProt accession number with optional PTM type filters
  • Returns: Complete PTM analysis with functional impact predictions and confidence scoring
get_pathway_data

Get detailed pathway information and metabolic context for a protein.

  • Purpose: Understand protein function in biological pathways and networks
  • Input: UniProt accession number with database preference (KEGG, Reactome, etc.)
  • Returns: Complete pathway networks with reactions, modules, and related proteins

🧬 DNA Analysis Tools (4 tools)

analyze_gc_content

Calculate GC percentage and nucleotide composition of a DNA sequence.

  • Purpose: Analyze sequence composition and identify compositional bias
  • Input: DNA sequence string
  • Returns: GC content, AT content, nucleotide counts, skew analysis, and sequence characteristics
find_restriction_sites

Identify restriction enzyme cut sites in DNA sequence using REBASE database motifs.

  • Purpose: Find restriction enzyme recognition sites for cloning and molecular biology
  • Input: DNA sequence and optional enzyme list
  • Returns: Restriction sites by enzyme, fragment analysis, and cutting pattern visualization
predict_orfs

Scan all 6 reading frames for start/stop codons to detect open reading frames (ORFs).

  • Purpose: Identify potential protein-coding regions in DNA sequences
  • Input: DNA sequence with minimum length threshold
  • Returns: ORF locations by reading frame, amino acid sequences, and statistical analysis
assemble_fragments

Assemble short DNA sequences into one using overlap-based merging.

  • Purpose: Reconstruct longer sequences from overlapping fragments
  • Input: Array of DNA fragments with optional overlap parameters
  • Returns: Assembled sequence, overlap analysis, and assembly statistics

🧬 Protein Sequence Tools (3 tools)

predict_protein_properties

Predict molecular weight, isoelectric point, amino acid composition, and other physicochemical properties.

  • Purpose: Calculate protein physical and chemical characteristics
  • Input: Protein sequence (amino acid string)
  • Returns: Molecular weight, pI, hydropathy, instability index, amino acid composition, and stability predictions
predict_transmembrane_regions

Identify transmembrane helices using hydropathy analysis and TMHMM-like algorithms.

  • Purpose: Predict membrane-spanning regions and protein topology
  • Input: Protein sequence with analysis parameters
  • Returns: Transmembrane helices, topology predictions, signal peptides, and localization analysis
scan_protein_motifs

Detect functional motifs and domains using PROSITE patterns and other databases.

  • Purpose: Find functional sites and regulatory elements in proteins
  • Input: Protein sequence with database preference
  • Returns: Functional motifs, phosphorylation sites, glycosylation sites, and regulatory predictions

🔍 Sequence Similarity Tools (5 tools)

blast_search

Run BLAST (nucleotide or protein) search against NCBI databases to find similar sequences.

  • Purpose: Find similar sequences and identify homologs
  • Input: Query sequence with database and program selection
  • Returns: BLAST hits with alignments, E-values, bit scores, and statistical analysis
psi_blast_search

Run PSI-BLAST for deeper homology detection using iterative profile construction.

  • Purpose: Detect distant homologs through profile-based searching
  • Input: Protein sequence with iteration parameters
  • Returns: Profile-enhanced hits, iteration summary, and remote homolog detection
align_sequences_global

Perform Needleman-Wunsch global alignment to compare two sequences end-to-end.

  • Purpose: Align entire sequences to compare overall similarity
  • Input: Two sequences with scoring parameters
  • Returns: Global alignment with identity, similarity, gaps, and quality assessment
align_sequences_local

Perform Smith-Waterman local alignment to find the best local similarity between sequences.

  • Purpose: Find regions of local similarity between sequences
  • Input: Two sequences with scoring parameters
  • Returns: Local alignment with similarity regions and coverage analysis
generate_dotplot

Generate dot plot visualization for pairwise sequence comparison to identify similarity patterns.

  • Purpose: Visualize sequence similarity patterns and detect rearrangements
  • Input: Two sequences with window and threshold parameters
  • Returns: Dot plot coordinates, similarity regions, and pattern analysis

🧬 Multiple Alignment Tools (4 tools)

multiple_sequence_alignment

Align 2-20 protein or nucleotide sequences using progressive alignment algorithms.

  • Purpose: Create multiple sequence alignments for comparative analysis
  • Input: Array of sequences with alignment parameters
  • Returns: Multiple alignment with conservation analysis and quality metrics
highlight_conserved_regions

Find and analyze conserved regions in a multiple sequence alignment.

  • Purpose: Identify functionally important conserved regions
  • Input: Aligned sequences with conservation thresholds
  • Returns: Conserved regions, consensus sequences, and functional predictions
generate_sequence_logo

Create sequence logo data from multiple alignment to visualize conservation patterns.

  • Purpose: Generate conservation logos for motif visualization
  • Input: Aligned sequences with information content thresholds
  • Returns: Logo data with information content, residue frequencies, and motif analysis
export_alignment

Export multiple sequence alignment in various formats (FASTA, PHYLIP, Clustal, MSF).

  • Purpose: Convert alignments to different formats for external tools
  • Input: Aligned sequences with format selection
  • Returns: Formatted alignment file with usage instructions

🏗️ Structure & RNA Tools (4 tools)

get_protein_structure

Retrieve 3D protein structure data from PDB database with comprehensive metadata.

  • Purpose: Get experimental protein structure information
  • Input: PDB ID (4-character code)
  • Returns: Structure metadata, chain information, ligands, resolution, and experimental details
analyze_secondary_structure

Analyze protein secondary structure from PDB data or predict from sequence.

  • Purpose: Determine protein secondary structure elements
  • Input: Protein sequence with optional PDB structure
  • Returns: Secondary structure composition, helices, sheets, turns, and structural classification
predict_rna_secondary_structure

Predict RNA secondary structure using thermodynamic algorithms.

  • Purpose: Predict RNA folding and stability
  • Input: RNA sequence
  • Returns: Secondary structure, base pairs, loops, stems, and thermodynamic analysis
scan_rna_motifs

Identify functional RNA motifs and regulatory elements in sequence.

  • Purpose: Find functional RNA elements and regulatory sites
  • Input: RNA sequence with structure context option
  • Returns: Regulatory motifs, structural elements, and functional predictions

🌳 Phylogenetics Tools (2 tools)

build_phylogenetic_tree

Build phylogenetic tree from multiple sequences using Neighbor-Joining, UPGMA, or Maximum Parsimony.

  • Purpose: Construct evolutionary trees from sequence data
  • Input: 3-50 sequences with method selection and bootstrap options
  • Returns: Phylogenetic tree in Newick format with branch lengths and support values
compare_phylogenetic_trees

Compare two phylogenetic trees using Robinson-Foulds distance and other metrics.

  • Purpose: Assess topological differences between trees
  • Input: Two sets of sequences for tree comparison
  • Returns: Tree comparison metrics, topological differences, and statistical assessment

📊 Documentation Tools (2 tools)

log_analysis_parameters

Record workflow parameters, data, and results for reproducibility and tracking.

  • Purpose: Document analysis workflows for reproducibility
  • Input: Tool name, parameters, input data, and results
  • Returns: Structured log entry with performance metrics and metadata
generate_resource_map

Create comprehensive guide of bioinformatics databases, tools, and workflow recommendations.

  • Purpose: Generate personalized resource guides and workflow recommendations
  • Input: Focus areas and analysis history
  • Returns: Curated database list, algorithm recommendations, workflow guides, and citations

🚀 使用 npx 快速启动（推荐）

使用 NPX 一键启动

npx bach-biotools-server

在 Cursor / Cherry Studio 中配置

无需安装，直接在 MCP 配置中使用：

{
  "mcpServers": {
    "biotools": {
      "command": "npx",
      "args": ["-y", "bach-biotools-server"]
    }
  }
}

保存配置后重启，npx 会自动从 npm 下载并运行最新版本的 bach-biotools-server。

包地址: https://www.npmjs.com/package/bach-biotools-server

Quick Start

Prerequisites

  • Node.js 18+
  • npm or yarn

Manual Installation

git clone https://github.com/BACH-AI-Tools/biotools-mcp-server.git
cd biotools-mcp-server
npm install
npm run build
npm start

Testing

npm run inspect

📖 Usage Example

// Search for BRCA1-related publications
{
  "tool": "search_pubmed",
  "arguments": {
    "term": "BRCA1 mutations breast cancer",
    "max_results": 10
  }
}

// Get detailed protein information
{
  "tool": "get_protein_entry",
  "arguments": {
    "accession": "P38398"  // BRCA1 protein
  }
}

// Analyze DNA sequence GC content
{
  "tool": "analyze_gc_content",
  "arguments": {
    "sequence": "ATCGATCGATCGATCG"
  }
}

// Build phylogenetic tree
{
  "tool": "build_phylogenetic_tree",
  "arguments": {
    "sequences": [
      {"id": "seq1", "sequence": "MKLLLLLL..."},
      {"id": "seq2", "sequence": "MKLLLLLL..."},
      {"id": "seq3", "sequence": "MKLLLLLL..."}
    ],
    "method": "neighbor-joining",
    "bootstrap_replicates": 100
  }
}

🔧 Configuration

如果需要手动配置（不使用 npx），可以添加到 MCP 配置：

{
  "mcpServers": {
    "biotools": {
      "command": "node",
      "args": ["/path/to/biotools-mcp-server/build/index.js"]
    }
  }
}

推荐使用上面的 npx 方式，更简单且自动获取最新版本。

📋 Features

  • 37 comprehensive tools covering all major bioinformatics analysis types
  • 11+ major biological databases integrated (PubMed, UniProt, NCBI, KEGG, PDB, etc.)
  • Complete research workflows from literature review to phylogenetic analysis
  • Advanced algorithms including BLAST, multiple alignment, phylogenetics, and structure prediction
  • Reproducible analysis with comprehensive logging and documentation tools
  • Flexible input/output supporting multiple sequence formats and databases

🧬 Analysis Categories

Sequence Analysis

  • DNA/RNA composition and structure analysis
  • ORF prediction and restriction mapping
  • Protein property prediction and motif scanning
  • Transmembrane region and secondary structure prediction

Comparative Analysis

  • Sequence similarity searching (BLAST, PSI-BLAST)
  • Pairwise and multiple sequence alignment
  • Phylogenetic tree construction and comparison
  • Conservation analysis and motif discovery

Database Integration

  • Literature mining from PubMed
  • Protein data from UniProt
  • Nucleotide sequences from GenBank/RefSeq/Ensembl
  • Structural data from PDB
  • Pathway information from KEGG and Reactome

Advanced Analytics

  • RNA secondary structure prediction
  • Post-translational modification analysis
  • Cross-database reference mapping
  • Fragment assembly and sequence reconstruction

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Test with npm run inspect
  5. Submit a pull request

📄 License

MIT License - see LICENSE file for details.