Enrichr MCP Server

Perform gene set enrichment analysis using Enrichr with support for multiple gene set libraries. Use this tool when you need to: - analyze gene functions - test enrichment across different databases - find biological processes/pathways/diseases - perform functional enrichment - analyze gene sets - identify overrepresented terms - run enrichment analysis - perform gene ontology analysis - test for enriched biological terms - analyze gene list functionality across multiple databases. Returns only statistically significant terms (adjusted p < 0.05) to reduce context usage. Supports GO, pathways, disease, tissue, drug, and many other gene set libraries available in Enrichr. This server is configured with the following default libraries: - GO_Biological_Process_2025: Gene Ontology terms describing biological objectives accomplished by gene products. - KEGG_2021_Human: Metabolic and signaling pathways from Kyoto Encyclopedia of Genes and Genomes for human. - Reactome_2022: Curated and peer-reviewed pathways from Reactome covering signaling, metabolism, gene expression, and disease. - MSigDB_Hallmark_2020: Hallmark gene sets representing well-defined biological states and processes from MSigDB. - ChEA_2022: ChIP-seq experiments from GEO, ENCODE, and publications identifying transcription factor-gene interactions from human and mouse. - GWAS_Catalog_2023: Genome-wide association study results from NHGRI-EBI GWAS Catalog linking genes to traits. - Human_Phenotype_Ontology: Standardized vocabulary of phenotypic abnormalities associated with human diseases. - STRING_Interactions_2023: Protein interactions from STRING database including experimental and predicted. - DrugBank_2022: Drug targets from DrugBank including approved drugs and experimental compounds. - CellMarker_2024: Manually curated cell type markers from CellMarker database for human and mouse. The model should select the most relevant library/libraries from the list below based on the user's query.

Perform Gene Ontology (GO) Biological Process enrichment analysis to understand what biological functions and processes are overrepresented in your gene list. This tool helps researchers interpret gene expression data, identify statistically significant biological processes, and uncover functional implications of genes from RNA-seq, microarray, or other high-throughput experiments. Use this when you need to: analyze gene functions, find enriched biological processes, perform functional profiling of gene lists, understand molecular mechanisms, interpret differentially expressed genes (DEGs), discover key biological pathways, annotate gene lists functionally, characterize gene sets involved in specific phenotypes, connect genes to their biological roles, or investigate what your genes do. The tool performs over-representation analysis (ORA) using the Enrichr API and GO Biological Process 2025 database, returning only statistically significant terms (adjusted p-value < 0.05) to provide meaningful biological insights while managing context usage. Perfect for transcriptomics analysis, systems biology studies, drug target identification, biomarker discovery, and understanding disease mechanisms. For multi-library analysis across different databases (KEGG, Reactome, etc.), use enrichr_analysis instead.