Research-data acquisition MCP — find and fetch datasets across archives, omics registries, and literature
Project description
🔎 data-aggregator-mcp
One MCP server to find and fetch research data across archives, omics registries, and literature — behind a single normalized model.
search one query across Zenodo, DataCite (Dryad / Figshare / Dataverse /
OSF / Mendeley), NCBI omics (GEO / SRA / BioProject), and literature
(PubMed / OpenAIRE) — deduplicated, normalized, and cross-linked. resolve any
hit to its file manifest, citation, and the data it points at. fetch it to
disk with checksum verification.
mcp-name: io.github.musharna/data-aggregator-mcp
✨ Why this
Most data MCPs wrap a single source. This one unifies them behind four tools
and one DataResource model, so an agent searches once and gets back comparable
records:
- Multi-domain, one model — generalist archives + raw omics + literature, deduplicated by DOI (the fetchable record wins over bare metadata).
- Taxonomy synonym expansion —
organism="Orobanche aegyptiaca"also matchesPhelipanche aegyptiaca(NCBI Taxonomy), so a species rename doesn't cost you results. - Paper → data bridge — resolve a paper and get links to the GEO / SRA / BioProject / DataCite records it produced.
- Verified fetch — streams to disk with md5 verification where the source exposes a checksum, optional archive unpacking, and a fail-loud integrity sniff that rejects an HTML paywall page served as a "PDF".
- Citations, access & full text — render a citation in any CSL style, get
normalized access/license, and pull open-access full text — all in one
resolve.
⚡ Quickstart
Run with no install:
uvx data-aggregator-mcp
Register with Claude Code:
claude mcp add data-aggregator -- uvx data-aggregator-mcp
A typical agent flow:
search("drought stress RNA-seq", organism="Sorghum bicolor")
→ [ geo:GSE..., sra:SRX..., zenodo:..., pubmed:... ] # deduped, taxa-normalized
resolve("sra:SRX079566")
→ DataResource{ files: [ENA FASTQ urls…], access: "open", taxa: [...] }
fetch("sra:SRX079566", dest="./data")
→ ["./data/SRX079566_1.fastq.gz", …] # md5-verified
Other ways to run (pip, python -m, raw client config)
pip install data-aggregator-mcp
data-aggregator-mcp # or: python -m data_aggregator_mcp
Add to a client's MCP config (e.g. Claude Desktop claude_desktop_config.json):
{
"mcpServers": {
"data-aggregator": {
"command": "uvx",
"args": ["data-aggregator-mcp"],
"env": { "NCBI_API_KEY": "your-optional-key" }
}
}
}
🗂️ Sources
| Source | Discover | Fetch | Checksum |
|---|---|---|---|
| Zenodo | ✅ | ✅ | md5 |
| DataCite → Figshare | ✅ | ✅ | md5 |
| DataCite → Dataverse | ✅ | ✅ | md5 |
| DataCite → OSF | ✅ | ✅ | md5 |
| DataCite → Dryad | ✅ | manifest only¹ | sha-256 (listed) |
| DataCite → Mendeley & others | ✅ | — | — |
| NCBI SRA | ✅ | ✅ (ENA FASTQ) | md5 |
| NCBI GEO | ✅ | ✅ (suppl/) |
none² |
| NCBI BioProject | ✅ | → SRA links | — |
| PubMed / OpenAIRE | ✅ | ✅ (OA full text) | none² |
¹ Dryad downloads are token / bot-challenge gated, so fetch fails loud;
resolve still lists the files.
² No upstream checksum — fetch verifies content-type instead (rejects an HTML
page served in place of a binary).
🛠️ Tools
search(query, size?, sources?, organism?)
Fan out across all wired sources in parallel and return compact DataResource
records, deduped by DOI. Per-source failures land in errors{} — never silently
dropped.
organism— expand the query with NCBI-Taxonomy synonyms; the expansion is echoed intaxon_expansion, and results carry normalizedtaxa[]({taxid, name}) plus adescribed_inlink to plant-genomics-mcp for plant taxa.sources— restrict the fan-out, e.g.["omics"].size— max results (1–50).
resolve(id)
Full record + files manifest. Routes by id shape — zenodo:7654321, a bare DOI,
datacite:10.5061/dryad.x, an omics id (sra:SRX079566, geo:GSE332789,
bioproject:PRJNA1468572), or a literature id (pubmed:34320281,
openaire:<id>). Attaches, where available:
files[]— ENA FASTQ manifest (SRA), GEOsuppl/, or the host repo's native manifest (Figshare / Dataverse / OSF / Dryad).links[]— paper → data:pubmed:→sra:/geo:/bioproject:(NCBI elink);openaire:→datacite:(ScholeXplorer Scholix).access/license— normalized status (open/embargoed/restricted/closed/unknown) and license where the source exposes it.identifiers— normalized{pmid, pmcid, doi}, plus an open-access full-textFileEntry(EuropePMC XML, or an Unpaywall PDF fallback) for papers.citation— passcite=<format>:bibtex,ris,csl-json, or any CSL style name (apa,mla,vancouver, …). DOI records use content negotiation; others render CSL-JSON from metadata. Off by default; failures degrade quietly.
fetch(id, dest?, files?, max_bytes?, force?, extract?)
Download files to disk and return their paths. Streams under a max_bytes guard
(force to override) with md5 verification wherever a checksum exists.
files— restrict to a subset of the resolved manifest.extract— unpack downloaded zip / tar archives in place, guarded against path traversal and runaway extracted size. Off by default.- Unverified fetches (GEO
suppl/, literature full text) get a content-type sniff that fails loud if a declared binary is actually an HTML page. - Fetchable: Zenodo, SRA, GEO, DataCite-hosted Figshare /
Dataverse / OSF, and literature open-access full text. Dryad
and other DataCite repos are discovery-only and raise
FetchNotSupportedError.
list_sources()
Wired sources with their capabilities — layer, kinds, supported filters, fetchability, id examples, auth, and rate limits.
⚙️ Configuration
Both optional, set via environment variables:
NCBI_API_KEY— raises the NCBI E-utilities rate limit (3 → 10 req/s) used by the omics, literature, and taxonomy lookups.UNPAYWALL_EMAIL— enables the Unpaywall fallback leg of literature full-text retrieval (the EuropePMC leg works without it).
🧪 Develop
uv venv && uv pip install -e ".[dev]"
uv run pytest -q
uv run ruff check src tests
DATA_AGGREGATOR_MCP_LIVE=1 uv run pytest -k live -q # real-API probes
The README demo (examples/assets/demo.svg) is recorded network-free from
examples/_demo_stdio.py — see the header of that file to re-record.
License
MIT — see LICENSE.
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