Markcrawl vs Enrichr MCP Server

Single-page and whole-site crawling

Clean Markdown and JSONL output

Citation metadata with access dates

URL filtering and crawl resumption

Optional binary, image, and screenshot downloads

Local embeddings without an API key

enrichr_analysis

Perform gene set enrichment analysis using Enrichr across multiple gene set libraries. Returns only statistically significant terms (adjusted p < 0.05). Configured default libraries: - GO_Biological_Process_2026: Current Gene Ontology biological process terms — biological objectives and programs accomplished by gene products. The default choice for GO biological process enrichment. - KEGG_2026: Current KEGG metabolic and signaling pathways — the up-to-date KEGG release; prefer over older KEGG vintages. - Reactome_Pathways_2024: Current Reactome release — curated, peer-reviewed pathways for 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_2025: Current genome-wide association study results from the NHGRI-EBI GWAS Catalog, linking genes to human traits and diseases. - 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 — druggable targets and their compounds. - CellMarker_2024: Manually curated cell type markers from CellMarker database for human and mouse. Any library in Enrichr's live catalog is accepted. Enrichr adds and retires libraries continuously, so do not rely on a memorized list: call suggest_libraries, or read the enrichr://libraries resource, to discover the libraries that currently exist. Pass 'background' to test against a custom background gene set (for example, only the genes expressed in your assay) instead of Enrichr's whole-genome default. This is the statistically correct choice whenever the gene list was drawn from a restricted universe, and it matters: the whole-genome default can overstate significance by many orders of magnitude. Each library's result reports 'backgroundCorrected'. If Enrichr's background service is unavailable the result falls back to uncorrected whole-genome p-values, flagged with a warning — treat those numbers as inflated and re-run rather than reporting them as background-corrected.