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musharna/data-aggregator-mcp

by Various

Unified research-data acquisition MCP — search & fetch datasets across Zenodo, DataCite, NCBI omics (GEO/SRA/BioProject), and literature (PubMed/OpenAIRE) behind one normalized mod

M

MCP

musharna/data-aggregator-mcp

Added 8 June 2026

#bioinformatics #datacite #datasets #mcp #model-context-protocol #ncbi #pubmed #python

Overview

A unified MCP server that searches and fetches datasets across Zenodo, DataCite, NCBI omics (GEO/SRA/BioProject), and literature (PubMed/OpenAIRE). It normalizes results into a single model, enabling consistent research-data acquisition from multiple sources.

Best for

Best for
Researchers and developers building data aggregation tools for scientific datasets

Use cases

  • Aggregate dataset metadata from multiple scientific repositories in one query
  • Fetch normalized research data for literature and omics studies
  • Integrate cross-repository search into data analysis pipelines

Notes

A unified MCP server that searches and fetches datasets across Zenodo, DataCite, NCBI omics (GEO/SRA/BioProject), and literature (PubMed/OpenAIRE). It normalizes results into a single model, enabling consistent research-data acquisition from multiple sources.

1 stars on GitHub. Last updated 2026-06-07. Licensed MIT.

Use cases

  • Aggregate dataset metadata from multiple scientific repositories in one query
  • Fetch normalized research data for literature and omics studies
  • Integrate cross-repository search into data analysis pipelines

Pros

  • Unifies multiple major scientific data sources behind a single interface
  • Reduces boilerplate for multi-source research data retrieval
  • Open source with a simple MCP protocol for integration

Cons

  • Very early stage with only 1 GitHub star and limited community adoption
  • Dependency on external APIs that may have rate limits or downtime
  • No built-in caching or offline support for repeated queries

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Unifies multiple major scientific data sources behind a single interface
  • Reduces boilerplate for multi-source research data retrieval
  • Open source with a simple MCP protocol for integration

Cons

  • Very early stage with only 1 GitHub star and limited community adoption
  • Dependency on external APIs that may have rate limits or downtime
  • No built-in caching or offline support for repeated queries