"Exploration of Free Cursor Thinking Concept" matching MCP servers:

Control PyMOL from Cursor via natural language. Enables describing molecular structures and styles in plain language to execute PyMOL commands through XML-RPC.

Enables AI agents to resolve scientific (Latin binomial) names to Open Tree of Life taxon IDs, retrieve full taxonomic details, synonyms, and ancestor lineages, and find the most recent common ancestor of up to 10 taxa within the synthetic tree of life. Runs keylessly over a hosted gateway endpoint or locally via npx.

Enables AI-powered genomic variant analysis including variant impact prediction, regulatory element discovery, and batch variant scoring. Currently operates in mock mode as a proof-of-concept awaiting the public release of Google DeepMind's AlphaGenome API.

A Model Context Protocol server that lets you drive PyMOL with natural language, enabling structure loading, analytical views, measurements, and protein exploration through AI assistants.

Enables read-only exploration of Copick cryo-ET projects and discovery/validation of copick CLI commands for building processing pipelines.

Federates 13 gene-related MCP backends (gnomAD, GTEx, etc.) behind a single Streamable HTTP endpoint with collision-free namespacing and search-based tool discovery.

Enables users to look up yeast genes/loci, search genes and alleles by free text, and retrieve Gene Ontology annotations from the Saccharomyces Genome Database.

Resolves free-text condition and disease strings — trial-registry condition fields, drug-label indications, hand-typed wording — onto the Mondo Disease Ontology, returning the best term id and label along with a trustworthy match-quality label (exact label/synonym, broader, narrower, fuzzy, or no-match) plus cross-ontology xrefs. Optionally expands a resolved term to all of its descendant ids for building subtype-inclusive registry filters.

Enables keyless lookup of marine species and genus names, retrieval of full taxonomic lineages by AphiaID, and common vernacular names across languages via MCP tools.

Enables LLMs and AI agents to query a biomedical knowledge graph stored in RedisGraph, with tools for concept search, synonym enrichment, and study variable discovery through semantic relationships.

Grounds gene-nomenclature work in the HUGO Gene Nomenclature Committee (HGNC) dataset, enabling resolution of gene symbols and IDs to canonical HGNC identifiers, plus cross-references and batch operations.

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.

MCP server that exposes the UniProt REST API to LLM clients, enabling search and retrieval of protein data via tools like search_uniprotkb, get_entry, and map_ids.

Enables discovery, inspection, and cross-referencing of Protein Data Bank structures through three RCSB APIs: search, data, and sequence coordinates.

Enables editing and querying of Gene Ontology Causal Activity Models (GO-CAMs) through the Barista API. Supports model creation, individual and fact management, evidence addition, and causal pathway construction for biological knowledge representation.

MCP server that provides tools for querying the Human Phenotype Ontology (HPO) including term lookup, hierarchy exploration, cross-ontology mappings, and gene-phenotype-disease associations, all grounded in a local SQLite database for fast offline lookups.

Enables users to search multiple public life-science data repositories (GEO, ENA, CELLxGENE, PRIDE, DataCite) with a single query, returning a unified ranked list of datasets with ontology-expanded terms and per-source error reporting.

Converts natural language queries into Cypher queries against the NASA GeneLab Knowledge Graph, enabling AI-assisted analysis of spaceflight experiments and their biological effects.

MCP server for single-cell lineage tracing analysis, enabling conversational reconstruction, visualization, and heritability analysis of lineage trees using Cassiopeia, pycea, and treedata.

Enables gene set enrichment analysis using the Enrichr API across hundreds of gene set libraries including Gene Ontology, pathways, diseases, tissues, drugs, and transcription factors. Returns only statistically significant results for interpretation.