Enrichr MCP Server

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.

Installation

Claude Desktop

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.

Cursor / VS Code

Use the buttons below to install with default settings:

Claude Code

claude mcp add enrichr-mcp-server -- npx -y enrichr-mcp-server

Or install as a Claude Code plugin:

/plugin install enrichr-mcp-server

Smithery

npx -y @smithery/cli install enrichr-mcp-server --client claude

Manual Configuration

Add to your MCP client config (e.g., .cursor/mcp.json):

{
  "mcpServers": {
    "enrichr-server": {
      "command": "npx",
      "args": ["-y", "enrichr-mcp-server"]
    }
  }
}

Features

• Two Tools: enrichr_analysis for running enrichment, suggest_libraries for discovering relevant libraries

• Library Catalog: Browse 200+ libraries by category via MCP resources

• Guided Workflow: enrichment_analysis prompt for end-to-end analysis with interpretation

• 22 Library Categories: Programmatic category mapping for all libraries (pathways, cancer, kinases, etc.)

• Parallel Library Queries: All libraries queried in parallel for fast multi-database analysis

• Structured Output: Returns both human-readable text and structured JSON for programmatic use

• Configurable Output Formats: Detailed, compact, or minimal to manage token usage

• TSV Export: Save complete results to TSV files

Tools

suggest_libraries

Discover the most relevant Enrichr libraries for a research question. Use this before enrichr_analysis to pick the best libraries for your specific topic. No network call needed — searches locally across all library names and descriptions.

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:

• Ranked list of libraries with relevance scores, categories, and descriptions

• Structured JSON with suggestions array

enrichr_analysis

Perform enrichment analysis across multiple Enrichr libraries in parallel.

Parameters:

• genes (required): Array of gene symbols (e.g., ["TP53", "BRCA1", "EGFR"]) — minimum 2

• libraries (optional): Array of Enrichr library names to query (defaults to configured libraries)

• description (optional): Description for the gene list

• maxTerms (optional): Maximum terms per library (default: 50)

• format (optional): Output format: detailed, compact, minimal

• outputFile (optional): Path to save complete results as TSV file

Returns:

• Text content with formatted significant terms (name, p-values, odds ratio, combined score, overlapping genes)

• Structured JSON output with full result data

Resources

Prompts

enrichment_analysis

Guided 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 separated

• context (optional): Research context for library selection (triggers suggest_libraries step)

Library Categories

All 200+ libraries are organized into 22 categories:

Use suggest_libraries to search across all categories, or read enrichr://libraries/{category} for the full list in any category.

Configuration

Command Line Options

Format Options

• 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)

Environment Variables

Note: CLI arguments take precedence over environment variables.

Multiple Server Instances

Set up different instances for different research contexts:

{
  "mcpServers": {
    "enrichr-pathways": {
      "command": "npx",
      "args": ["-y", "enrichr-mcp-server", "-l", "GO_Biological_Process_2025,KEGG_2021_Human,Reactome_2022"]
    },
    "enrichr-disease": {
      "command": "npx",
      "args": ["-y", "enrichr-mcp-server", "-l", "Human_Phenotype_Ontology,OMIM_Disease,ClinVar_2019"]
    }
  }
}

Popular Libraries (Default)

When using the default -l pop configuration:

API Details

This server uses the Enrichr API:

• Add List Endpoint: https://maayanlab.cloud/Enrichr/addList

• Enrichment Endpoint: https://maayanlab.cloud/Enrichr/enrich

• Supported Libraries: All libraries available through the Enrichr web interface

Development

npm run build          # Build TypeScript
npm test               # Run tests (unit + integration + MCP protocol)
npm run test:watch     # Run tests in watch mode
npm run watch          # Auto-rebuild on file changes
npm run inspector      # Debug with MCP inspector

Requirements

• Node.js 18+

• Internet connection for Enrichr API access

License

MIT

References

• 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

• Enrichr

• Model Context Protocol

• MCP TypeScript SDK