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data-aggregator-mcp

data-aggregator-mcp

by musharna

data-aggregator-mcp

data-aggregator-mcp is an MCP server that lets AI agents search 21 public research-data sources at once and get back one deduplicated list.

CI PyPI Python License DOI Glama

Install

Claude Code:

claude mcp add data-aggregator -- uvx data-aggregator-mcp

One-click install for VS Code or Cursor:

Install in VS Code Install in Cursor

Any other MCP client: run uvx data-aggregator-mcp as a stdio server.

Claude Desktop (claude_desktop_config.json) and most other clients:

{
  "mcpServers": {
    "data-aggregator": {
      "command": "uvx",
      "args": ["data-aggregator-mcp"],
      "env": { "NCBI_API_KEY": "your-optional-key" }
    }
  }
}

With pip:

pip install data-aggregator-mcp
data-aggregator-mcp        # or: python -m data_aggregator_mcp

operate (SQL and previews over remote files) needs an optional extra: pip install "data-aggregator-mcp[operate]".

Related MCP server: plant-genomics-mcp

What you can ask

"Find transcriptomes for Orobanche aegyptiaca." The species was renamed; NCBI Taxonomy maps it to Phelipanche aegyptiaca, and the sources that accept synonyms match both names (the demo above).

"What data came out of this paper?"

resolve("pubmed:40098680")
  links  geo:GSE284240, bioproject:PRJNA1198054, and 18 sra: experiments
  files  PMC11910882.xml   (open-access full text)

"Can I train a model on this dataset?"

resolve("hf:scikit-learn/iris", use="ml-training")
  license_compat  ALLOW: "CC0-1.0 grants the permission(s) required for
                  ml-training: commercial-use, modifications"

The verdict is read from the record's license metadata; it is advice, not a legal opinion. resolve also renders citations in any CSL style, checks Crossref for retractions, scores FAIRness, and exports Croissant or RO-Crate. relate reports how a set of records connect (a shared accession, DOI or explicit link).

How a search runs

A source that fails is named in the result's errors, never dropped silently.

What each source can fetch, and which checksum it is verified against: docs/sources.md.

What the benchmark shows

An agent using this server was compared with the same agent using web search, on held-out dataset-finding tasks scored by recall against independently built answer keys, 3 runs per task. Mean recall, server against web search: Claude Haiku 5.5 0.47 against 0.32 (higher on 8 of 10 tasks), Sonnet 5.5 0.54 against 0.52 (5 of 10), Opus 5.5 0.72 against 0.58 (6 of 10). With Haiku the server met round 5's pre-registered "better" rule (higher on at least 8 of 10 tasks, interval above 0) but not the stricter bar for a win across model tiers (9 of 10, Holm-adjusted sign test, p = 0.22); Sonnet and Opus met neither. With Opus it returned fewer wrong entries and answered in about a third of the time; with Sonnet it was faster on 9 of 10 tasks; with Haiku it cost less on 9 of 10.

The two latest Opus 5.5 rounds (2026-10-19, rules written before the runs):

Tools given to the agent

Round 4 (5 tasks)

Round 5 (10 tasks)

this server

0.66

0.72

web search

0.66

0.58

ToolUniverse

0.59

0.63

no tools (memory only)

0.02

0.09

In round 5 this server was higher on 6 of 10 tasks, where the pre-set rule needed 8. Web search hit the 15-minute limit in 11 of 30 runs, and a run that times out scores 0. In runs that finished, web search found more (0.90). The server answered in 203 s against 599 s (median), with fewer tool calls and fewer wrong entries (precision 0.95 against 0.62). It cost more per run than web search ($2.08 against $1.51, API-equivalent).

We chose the tasks and ran the benchmark ourselves; an outside re-run is welcome. Method and controls: benchmarks/h2h; the Haiku and Sonnet runs: 2026-10-20 re-run.

Compared with other servers

Server

Searches

One search

Checksum

Remote SQL

License check

data-aggregator-mcp

21 sources: data archives, omics, papers

yes, DOI dedup

where published

yes

yes

Mobus

20 general and ML data platforms

yes

—

row preview

yes

paper-search-mcp

papers: arXiv, PubMed, OpenAlex, Crossref and more

yes, deduplicated

—

—

—

ToolUniverse

1000+ tools: models, datasets, APIs, packages

papers

—

—

—

BioMCP

genes, variants, trials, drugs, proteins, papers

yes (search all)

—

—

—

— means the project's README doesn't describe it (READMEs read 2026-10-04). Where they are ahead: BioMCP reaches clinical trials, ChEMBL and AlphaFold; ToolUniverse covers far more ground overall; paper-search-mcp covers far more literature sources; Mobus also compares datasets and checks schema compatibility. More: docs/POSITIONING.md.

Reference and development

Every tool and parameter, the HTTP transport (--transport http) and the environment variables: docs/reference.md. Sources that need every word of a query, and how they are searched: every-word sources. Tests, linting and the live API probes: CONTRIBUTING.md.

License

MIT. See LICENSE.

Available Tools

6 tools
fetchA

Download a resource's files to local disk and return the PATHS (never the file contents). Tool names in this text are this server's own; your client may list them with a prefix (in Claude Code, mcp__data-aggregator__resolve): call the name it lists. Fetchable backends: Zenodo (md5-verified); SRA via ENA FASTQ (md5-verified); GEO supplementary files (unverified); DataCite sub-repos — Figshare/Dataverse/OSF (md5-verified), OpenNeuro (snapshot manifest, unverified), Dryad is manifest-only (resolve lists files, fetch fails loud), Mendeley + other DataCite repos fail loud; PubMed/OpenAIRE open-access full text (EuropePMC XML / Unpaywall PDF, unverified); HuggingFace Hub (unverified); DataONE Member-Node objects (md5/SHA-256-verified); OmicsDI — PRIDE (unverified) + MetaboLights (sha-256-verified) only, MassIVE/jPOST/iProX/PeptideAtlas/Panorama Public/GNPS/Metabolomics Workbench fail loud; DANDI dandisets (302→S3, sha-256-verified); CZ CELLxGENE H5AD/RDS assets (unverified); OpenML ARFF (md5-verified); RCSB PDB .cif/.pdb structure files (unverified); UniProtKB FASTA (unverified); BioStudies study files (unverified); GBIF Darwin Core Archives (unverified); data.gov dataset distributions (unverified). Fails loud if selected files exceed max_bytes unless force=true. Verifies md5/SHA-256 where the source publishes one; files marked unverified get no integrity check. Writes a .dataresource.json sidecar.

Inputs: id (required), dest, files, force, +2 more. Returns: bytes, paths, resumed, skipped, +1 more

list_sourcesA
Read-only

List wired data sources and their capabilities (layer, kinds, supported filters, auth requirement, rate limit, status). Tool names in this text are this server's own; your client may list them with a prefix (in Claude Code, mcp__data-aggregator__resolve): call the name it lists. filters_supported names the search parameters a source applies itself: cursor = it pages past page 1; published_after/published_before/kind = filtered upstream, so its total is filtered (any other source is filtered after fetch); organism/disease/tissue/chemical/assay = the synonym expansion reaches its query.

Inputs: check_health. Returns: sources

operateA
Read-only

Inspect or query a remote tabular file (Parquet/CSV/TSV) WITHOUT downloading it. op='schema' returns columns+types; 'preview' a small sample; 'head' the first n rows; 'sql' a read-only SELECT against the file (exposed as the view 'data', e.g. "SELECT * FROM data WHERE x > 1"). op='peek' profiles every column WITHOUT downloading — type, null-rate, approximate distinct count, min/max, and numeric quartiles (a DuckDB SUMMARIZE; like head/sql it reads the whole file, so it honors the source-size ceiling). Tool names in this text are this server's own; your client may list them with a prefix (in Claude Code, mcp__data-aggregator__resolve): call the name it lists. Addresses a file by catalog id + file name (resolve the id first to see files[] and access_modes). Requires the [operate] extra; fails loud if the file is not an operable tabular file.

Inputs: id (required), op (required), n, file, +2 more.

relateA
Read-only

Given 2-10 resource ids, return metadata-level join/harmonization HINTS: how the datasets relate and on what key they could be joined. Detects shared accessions (BioProject/SRA/GEO), shared cross-identifiers (doi/pmid/pmcid), explicit links between the inputs, and version lineage. Tool names in this text are this server's own; your client may list them with a prefix (in Claude Code, mcp__data-aggregator__resolve): call the name it lists. HINTS ONLY — it does not read file columns, fetch files, or execute any join/merge/conversion; each hint names the shared value as evidence. Resolve ids first if you only have a search result. Per-id resolve failures are reported, not fatal.

Inputs: ids (required). Returns: Result of `relate(ids)` — metadata-level join/harmonization hints across a reso…

resolveA
Read-only

Tool names in this text are this server's own; your client may list them with a prefix (in Claude Code, mcp__data-aggregator__resolve): call the name it lists. Fetch the full DataResource for a known id (e.g. 'zenodo:1254563', 'datacite:10.5061/dryad.t4b8gtjgj', 'hf:davidcechak/Arabidopsis_thaliana_DNA_v0', a bare Zenodo record id, or a DOI), including the complete files[] manifest. Publication resolve also attaches normalized identifiers (pmid/pmcid/doi) and, when open access, a full-text file. Pass cite= to render a citation onto the result (citation field); omitted means no citation. Pass trust=true to attach retraction status (via Crossref) under trust{}. Pass fair=true to attach an RDA-grounded FAIRness score (0–100 + F/A/I/R sub-scores + actionable gaps) computed from the record under fair{}. Pass use= (commercial/redistribute/modify/ml-training) to attach a licence-compatibility advisory (ALLOW/REVIEW/DENY, not legal advice) under license_compat{}. Pass format=provenance for a one-call RO-Crate 1.1 data-availability dossier (under provenance{}) composing version-currency, licence+SPDX, FAIR score, retraction status, and the source/DOI/ID chain — it auto-attaches fair + trust.

Inputs: id (required), use, cite, fair, +2 more. Returns: id, doi, fair, kind, +32 more

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.4/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a distinct role: search discovers resources, resolve expands a known id, fetch downloads files, list_sources introspects coverage, operate analyzes remote tabular files without downloading, and relate compares resource ids. The adjacent file-related operations (resolve/fetch/operate) are explicitly scoped by description, so an agent can tell them apart.

    Naming Consistency4/5

    Five tools are bare lowercase verbs (search, resolve, fetch, operate, relate) and one is a snake_case verb_noun (list_sources), so the pattern is not perfectly uniform. It remains predictable and readable, with no chaotic mixing of camelCase or vague synonym pileups.

    Tool Count5/5

    Six tools is well-scoped for a read-only multi-archive research-data aggregator, and each tool covers a distinct workflow stage rather than duplicating another. The surface is neither thin nor bloated, and no obvious filler tool is present.

    Completeness5/5

    The set covers discovery, detailed record resolution, file download, source capability introspection, remote tabular inspection, and cross-resource relation hints, which is strong lifecycle coverage for this domain. Write/update operations are not expected for a read-only aggregation server, and remaining gaps look like minor conveniences rather than dead ends.

    Maintenance

    ActivityActive
    ResponsivenessMixed handling

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