"Examples of complex problem-solving using multiple tools" matching MCP servers:

Provides a collection of MCP servers for computational chemistry tasks including molecular generation and retrosynthesis. Also offers property prediction and molecule pricing capabilities.

Enables deep probabilistic analysis of single-cell omics data using scvi-tools through natural language. Supports SCVI for scRNA-seq analysis, SCANVI for cell type annotation, TOTALVI for multi-modal RNA/protein data, and PEAKVI for scATAC-seq analysis.

A specialized MCP server for Metal-Organic Framework research that provides tools for database searching, structural optimization, and energy calculations via ASE. It enables scientific workflows by allowing users to interact with MOF data and perform chemical simulations through a standard SSE interface.

Enables searching cryo-EM density maps in EMDB and raw datasets in EMPIAR, with filtering by resolution, keyword, and cross-referencing between the two databases.

A Model Context Protocol (MCP) server that provides access to the Protein Data Bank (PDB) - the worldwide repository of information about the 3D structures of proteins, nucleic acids, and complex assemblies.

Provides comprehensive access to the UCSC Genome Browser API, enabling queries of genomic data, DNA sequences, gene annotations, variants, and metadata across multiple species and assemblies.

Enables protein structure and sequence searches using FoldSeek, with support for multiple databases, automatic model downloads, and structured job management.

Enables complex searches, sequence mapping, genomic analysis, and metadata retrieval for non-coding RNA sequences from the RNAcentral database.

Exposes pipen bioinformatics pipelines as MCP tools, allowing AI assistants to discover and run complex workflows through a progressive disclosure interface.

Provides a natural language interface for scRNA-Seq analysis using the Scanpy library, supporting operations such as data preprocessing, clustering, and visualization. It enables AI agents and clients to perform complex single-cell transcriptomics workflows through the Model Context Protocol.

An MCP server for the gget bioinformatics library that enables AI assistants to perform complex genomics queries, including gene sequence retrieval, BLAST alignments, and protein structure predictions.

Validates the physical and chemical plausibility of ligand–protein docking poses using the PoseBusters tool.

Enables querying relationships between plant species, small molecules, and mitochondrial Complex I inhibitors by bridging natural-product, biodiversity, and PubMed datasets. Allows LLMs to perform structured searches and reasoning over biological data to identify potential plant-derived mitochondrial inhibitors.

Reproduces the in-silico toxicological profile of Heracleum sosnowskyi metabolites from Rassabina & Fedorov (2025) using open-source models for LD50 prediction, toxicity classification, chemical space clustering, and synthesis cost estimation.

Enables querying the CIViC database for clinical interpretations of cancer variants through tools like get_variant_evidence and get_variant_assertions.

Enables natural language interaction for scRNA-Seq analysis including preprocessing, clustering, and visualization using the CellRank library. It allows users and agents to perform complex genomic data tasks through standard MCP clients and frameworks.

An advanced integrated MCP server platform that combines 600+ tools and multiple biomedical databases to enable comprehensive information retrieval across molecules, proteins, genes, and diseases for accelerating therapeutic research.

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.

Enables analysis of bulk RNA-seq data using natural language queries, executing R and Python in a Docker container with automatic sample anonymization and privacy controls.