The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Enrichr MCP Server listing page.
A Model Context Protocol (MCP) server that provides gene set enrichment analysis using the Enrichr API. This server supports all available gene set libraries from Enrichr and returns only statistically significant results (corrected-$p$ < 0.05) for LLM tools to interpret.
Download the latest MCPB bundle (.mcpb file) and install it via ☰ (top left) -> File -> Settings, then drag and drop the file into the Settings window.
Use the buttons below to install with default settings:
Or install as a Claude Code plugin:
Add to your MCP client config (e.g., .cursor/mcp.json):
enrichr_analysis for running enrichment, suggest_libraries for discovering relevant librariesenrichment_analysis prompt for end-to-end analysis with interpretationsuggest_librariesDiscover the most relevant Enrichr libraries for a research question. Use this before enrichr_analysis to pick the best libraries for your specific topic. Searches Enrichr's live library catalog, so it never recommends a library that Enrichr has retired. When two libraries are equally relevant, the newer vintage ranks first (GO_Biological_Process_2026 over ..._2021).
Parameters:
query (required): Research context (e.g., "DNA repair", "breast cancer drug resistance")category (optional): Filter by category (e.g., cancer, pathways, kinases)maxResults (optional): Max results to return (default: 10, max: 50)Returns:
enrichr_analysisPerform enrichment analysis across multiple Enrichr libraries in parallel.
Parameters:
genes (required): Array of gene symbols (e.g., ["TP53", "BRCA1", "EGFR"]) — minimum 2libraries (optional): Array of Enrichr library names to query (defaults to configured libraries)background (optional): Custom background gene set — minimum 20 genes. See below.description (optional): Description for the gene listmaxTerms (optional): Maximum terms per library (default: 50)format (optional): Output format: detailed, compact, minimaloutputFile (optional): Path to save complete results as TSV fileReturns:
backgroundCorrected per libraryBy default Enrichr tests your gene list against the whole genome. If your genes
were drawn from a restricted universe — only the genes expressed in your tissue, or
a targeted panel — the whole-genome default overstates significance, often by many
orders of magnitude. Pass background with the universe the list was drawn from:
The difference is not cosmetic. For a 16-gene DNA-damage list, the top GO term moves from an adjusted p of 1.2e-16 (whole genome) to 1.8e-5 (48-gene background), and the number of "significant" terms drops from 359 to 10.
Background correction runs against Enrichr's separate speedrichr service, which is
intermittently unavailable. Failures are retried; if they persist, the library falls
back to uncorrected whole-genome p-values, and the result is flagged
backgroundCorrected: false with a loud WARNING in the text output. A fallback
result is never presented as if it were background-corrected.
| URI | Description |
|---|---|
enrichr://libraries | Full library catalog organized by category |
enrichr://libraries/{category} | Libraries for a specific category (e.g., enrichr://libraries/cancer) |
enrichment_analysisGuided workflow for gene set enrichment analysis. Accepts a gene list and optional research context, then walks through library selection, analysis, and interpretation.
Arguments:
genes (required): Gene symbols, comma or newline separatedcontext (optional): Research context for library selection (triggers suggest_libraries step)All 200+ libraries are organized into 22 categories:
| Category | Examples |
|---|---|
transcription | ChEA_2022, ENCODE_TF_ChIP-seq_2015, TRANSFAC_and_JASPAR_PWMs |
pathways | KEGG_2021_Human, Reactome_2022, WikiPathways_2023_Human, MSigDB_Hallmark_2020 |
ontologies | GO_Biological_Process_2025, GO_Molecular_Function_2025, Human_Phenotype_Ontology |
diseases_drugs | GWAS_Catalog_2023, DrugBank_2022, OMIM_Disease, DisGeNET |
cell_types | GTEx_Tissue_Expression_Up, CellMarker_2024, Tabula_Sapiens |
microRNAs | TargetScan_microRNA_2017, miRTarBase_2022, MiRDB_2019 |
epigenetics | Epigenomics_Roadmap_HM_ChIP-seq, JASPAR_2022, Cistrome_2023 |
kinases | KEA_2015, PhosphoSitePlus_2023, PTMsigDB_2023 |
gene_perturbations | LINCS_L1000_CRISPR_KO_Consensus_Sigs, CRISPR_GenomeWide_2023 |
metabolomics | HMDB_Metabolites, Metabolomics_Workbench_2023, SMPDB_2023 |
aging | Aging_Perturbations_from_GEO_down, GenAge_2023, Longevity_Map_2023 |
protein_families | InterPro_Domains_2019, Pfam_Domains_2019, UniProt_Keywords_2023 |
computational | Enrichr_Submissions_TF-Gene_Coocurrence, ARCHS4_TF_Coexp |
literature | Rummagene_signatures, AutoRIF, GeneRIF |
cancer | COSMIC_Cancer_Gene_Census, TCGA_Mutations_2023, OncoKB_2023, GDSC_2023 |
single_cell | Human_Cell_Landscape, scRNAseq_Datasets_2023, SingleCellSignatures_2023 |
chromosome | Chromosome_Location, Chromosome_Location_hg19 |
protein_interactions | STRING_Interactions_2023, BioGRID_2023, IntAct_2023, MINT_2023 |
structural | PDB_Structural_Annotations, AlphaFold_2023 |
immunology | ImmuneSigDB, ImmPort_2023, Immunological_Signatures_MSigDB |
development | ESCAPE, Developmental_Signatures_2023 |
other | MSigDB_Computational, HGNC_Gene_Families, Open_Targets_2023 |
Use suggest_libraries to search across all categories, or read enrichr://libraries/{category} for the full list in any category.
| Option | Short | Description | Default |
|---|---|---|---|
--libraries <libs> | -l | Comma-separated list of Enrichr libraries to query | pop |
--max-terms <num> | -m | Maximum terms to show per library | 50 |
--format <format> | -f | Output format: detailed, compact, minimal | detailed |
--output <file> | -o | Save complete results to TSV file | (none) |
--compact | -c | Use compact format (same as --format compact) | (flag) |
--minimal | Use minimal format (same as --format minimal) | (flag) | |
--help | -h | Show help message | (flag) |
detailed: Full details including p-values, odds ratios, and gene lists (default)compact: Term name + p-value + gene count (saves ~50% tokens)minimal: Just term name + p-value (saves ~80% tokens)| Variable | Description | Example |
|---|---|---|
ENRICHR_LIBRARIES | Comma-separated list of libraries | GO_Biological_Process_2025,KEGG_2021_Human |
ENRICHR_MAX_TERMS | Maximum terms per library | 20 |
ENRICHR_FORMAT | Output format | compact |
ENRICHR_OUTPUT_FILE | TSV output file path | /tmp/enrichr_results.tsv |
Note: CLI arguments take precedence over environment variables.
Set up different instances for different research contexts:
When using the default -l pop configuration:
| Library | Description |
|---|---|
GO_Biological_Process_2026 | Current Gene Ontology biological process terms. |
KEGG_2026 | Current KEGG metabolic and signaling pathways. |
Reactome_Pathways_2024 | Current Reactome release — curated, peer-reviewed pathways. |
MSigDB_Hallmark_2020 | Hallmark gene sets representing well-defined biological states and processes. |
ChEA_2022 | ChIP-seq experiments identifying transcription factor-gene interactions. |
GWAS_Catalog_2025 | Genome-wide association study results linking genes to traits. |
Human_Phenotype_Ontology | Standardized vocabulary of phenotypic abnormalities associated with human diseases. |
PPI_Hub_Proteins | Highly connected hub proteins from protein-protein interaction networks. |
DGIdb_Drug_Targets_2024 | Drug-gene interactions from the Drug Gene Interaction Database. |
CellMarker_2024 | Manually curated cell type markers for human and mouse. |
This server uses the Enrichr API:
https://maayanlab.cloud/Enrichr/addListhttps://maayanlab.cloud/Enrichr/enrichhttps://maayanlab.cloud/Enrichr/datasetStatistics — fetched at runtime and cached for 24h, so the library list is never stalehttps://maayanlab.cloud/speedrichr/api/{addList,addbackground,backgroundenrich}Note: Enrichr emits bare Infinity literals for the odds ratio when a term's overlap
with the background is complete — which is not valid JSON. This server parses those
responses correctly; a naive JSON.parse on the raw response will throw.
MIT
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 128(14).
Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma'ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research. 2016; gkw377.
Xie Z, Bailey A, Kuleshov MV, Clarke DJB., Evangelista JE, Jenkins SL, Lachmann A, Wojciechowicz ML, Kropiwnicki E, Jagodnik KM, Jeon M, & Ma'ayan A. Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. 2021. doi: 10.1002/cpz1.90