"How MCP Interacts with Blockchain Technology" matching MCP servers:

A Model Context Protocol server that interfaces with Biomart databases, allowing models to discover biological datasets, explore attributes/filters, retrieve biological data, and translate between different biological identifiers.

A framework that integrates Brain-Computer Interface technology with the Model Context Protocol to enable real-time neural signal processing and AI-powered interactions for healthcare, accessibility, and research applications.

Enables AI assistants to perform NCBI BLAST sequence similarity searches through natural language, supporting nucleotide and protein searches, custom database creation, and multiple output formats.

Enables AI assistants to perform quality control analysis on high-throughput sequencing data using FastQC and MultiQC. It supports single-file and batch processing of FASTQ/FASTA files and generates comprehensive, interactive summary reports.

An MCP server that enables AI assistants to generate, score, and analyze DNA sequences using the evo2 genomic foundation model. It supports multiple execution modes including local GPU, SLURM clusters, and the Nvidia NIM cloud API for tasks like variant effect prediction and sequence embedding.

MCP server for interacting with Galaxy bioinformatics platform, enabling AI assistants to connect to Galaxy instances, search and execute tools, manage workflows, and access other features.

An MCP server for running local LLMs using llama-cpp-python, with built-in tools for generating drug-like molecule SMILES.

Enables AI assistants to perform DNA/RNA sequence alignment using BWA (Burrows-Wheeler Aligner), supporting both short and long read alignment to reference genomes with indexing, BWA-MEM, and BWA-backtrack algorithms.

🔍 A biomedical literature annotation and relationship mining server based on PubTator3, providing convenient access through the MCP interface.

An MCP server that wraps R scripts for microbiome and amplicon (16S/ITS) analysis, providing tools for alpha/beta diversity, differential abundance, networks, and more, with PNG/PDF outputs.

MCP server for biological protein design, folding, and affinity prediction using Refua tools, with optional support for ADMET, clinical simulation, preclinical planning, wet-lab automation, and more.

An MCP server that enables language models to fetch protein information from the UniProt database, including protein details, sequences, functions, and structures.

MCP server for gene family analysis, providing tools for FASTA validation and PlantCARE cis-element prediction, with backend task management for AI agents.

An MCP server that grounds protein research in the UniProt SPARQL endpoint, providing tools for querying proteins, sequences, variants, diseases, and more via intent-named tools and raw SPARQL.

Enables language models to search biomedical literature, fetch sequences, and follow links across Entrez databases through eleven read-only tools wrapping the nine NCBI Entrez E-utilities. It handles URL building, pacing, redirects, response caps, and API-key redaction so queries can be answered without a browser or scraping.

Enables AI assistants to clean PDB structures and run initial-stage energy relaxation for biomolecular modeling. It provides a structured workflow for preparing structures before production molecular dynamics or coarse-graining pipelines.

MCP server that provides tools to query harmonized gene-disease validity data from the Gene Curation Coalition, supporting consensus and conflict detection for gene-disease assertions.

Enables genomic sequence analysis through the Evo 2 model, supporting DNA sequence scoring, embedding, generation, and variant effect prediction with multiple model checkpoints (7B, 40B, 1B parameters).

Enables DNA sequence analysis and variant effect prediction using Evo2-7B via MCP tools, providing forward inference, likelihood scoring, and batch variant comparison through natural language.

MCP server exposing REINVENT4 generative molecular-design operations as validated, LLM-callable tools for drug discovery, including sampling, transfer learning, reinforcement learning, and molecular analysis.