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data-aggregator-mcp

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Search public research-data archives, omics registries, and literature for datasets, software, publications, and sequencing data, with synonym expansion for organisms, diseases, tissues, chemicals, and assays.

Instructions

Search public research-data archives, omics registries, and the literature for datasets, software, publications, and sequencing data. Fans out across Zenodo, DataCite (Dryad, Figshare, Dataverse, OSF, Mendeley, OpenNeuro), NCBI omics (GEO, SRA, BioProject), literature (PubMed + OpenAIRE), HuggingFace Hub (datasets), DataONE (eco/environmental federation), OmicsDI (proteomics/metabolomics), RCSB PDB (macromolecular structures), GWAS Catalog (genotype-phenotype studies), OpenML (ML datasets), DANDI (neurophysiology dandisets), and CZ CELLxGENE (single-cell datasets). Returns compact DataResource records; per-source failures are reported in errors{}. Use resolve for the full record (SRA resolve attaches the ENA FASTQ manifest; publication resolve attaches links[] to datasets/accessions, normalized identifiers (pmid/pmcid/doi), and — when open access — a full-text file), then fetch to download files. Pass organism= to expand the query with NCBI-Taxonomy synonyms; results carry normalized taxa[] + plant cross-links. Pass disease= to expand the query with MeSH descriptor synonyms (e.g. 'breast cancer' also matches 'Breast Neoplasms'); the expansion is echoed in mesh_expansion. Pass tissue= to expand the query with UBERON synonyms (e.g. 'liver' also matches 'iecur'/'jecur'); the expansion is echoed in tissue_expansion. Pass chemical= to expand the query with ChEBI compound synonyms (e.g. 'caffeine' also matches '1,3,7-trimethylxanthine'); the expansion is echoed in chemical_expansion. Pass assay= to expand the query with EDAM assay/method synonyms (e.g. 'ChIP-seq' also matches 'ChIP-sequencing'); echoed in assay_expansion. Pass collapse_mirrors=true to opt into conservative cross-repo mirror collapse: same-dataset copies under different/no DOIs are folded into one record, with the folded copies annotated under mirrors[].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoKeep only results of this kind.
rankNoResult ordering. 'relevance' (default) = upstream/merged order. 'semantic' re-ranks the fetched page by embedding similarity to the query (needs EMBEDDING_API_BASE; degrades to relevance order with an errors['semantic'] note if unconfigured). In semantic mode pagination is window-based (each page consumes its full fetched window).relevance
sizeNoMax results (1-50, default 10)
assayNoOptional assay/method name. Resolved via EDAM topics (EBI OLS); the query is expanded with the canonical name + exact synonyms (e.g. 'ChIP-seq' also matches 'ChIP-sequencing'/'ChIP-exo'). An unknown term yields no expansion; an OLS failure surfaces in errors. The expansion is echoed in assay_expansion.
queryNoFree-text search query
cursorNoOpaque pagination token from a prior search's next_cursor. When set, all other search params are read from the cursor.
tissueNoOptional tissue/anatomy name. Resolved via UBERON (EBI OLS); the query is expanded with the canonical term + exact synonyms (e.g. 'liver' also matches 'iecur'/'jecur'). The expansion is echoed in tissue_expansion.
diseaseNoOptional disease/phenotype name. Resolved via MeSH (NCBI E-utilities); the query is expanded with the canonical descriptor + entry-term synonyms (e.g. 'breast cancer' also matches 'Breast Neoplasms'). The expansion is echoed in mesh_expansion.
sourcesNoRestrict fan-out to these sources (default: all). Available: zenodo, dataone, cellxgene, datacite, dandi, omics, literature, huggingface, omicsdi, openml, pdb, uniprot, gwas
chemicalNoOptional chemical/compound name. Resolved via ChEBI (EBI OLS); the query is expanded with the canonical name + exact synonyms (e.g. 'caffeine' also matches '1,3,7-trimethylxanthine'), capped to a bounded number of synonyms. An unknown term yields no expansion; an OLS failure surfaces in errors. The expansion is echoed in chemical_expansion.
organismNoOptional organism name. Resolved via NCBI Taxonomy; the query is expanded with the canonical name + synonyms (e.g. 'Orobanche aegyptiaca' also matches 'Phelipanche aegyptiaca'). The expansion is echoed in taxon_expansion.
provenanceNoOpt into a whole-search RO-Crate 1.1 Run Crate (default false). Attaches provenance_crate{} — a machine-readable manifest documenting this search: the query, the sources queried, the ontology expansions that fired, the per-source errors (a partial search is disclosed), and per-hit provenance for every result (version-currency, licence + normalized SPDX, FAIR score). Per-hit RETRACTION is omitted — it would need one Crossref call per hit; use per-record resolve(format=provenance) for that. Covers THIS search page only (intra-page; each page of a paginated search gets its own crate).
understandNoOpt into LLM query understanding: a free-text query is rewritten into a keyword core + structured params (organism/disease/tissue/chemical/assay, kind, year) before fan-out; extracted entities are validated by the same ontology resolvers (a hallucinated entity that doesn't resolve is simply dropped), explicit params you pass always win, and the interpretation is echoed in query_understanding. Requires an LLM endpoint (LLM_API_BASE); with none configured the search runs unchanged and notes it in errors['understand'].
multi_queryNoOpt into diverse multi-query recall expansion: an LLM generates up to a few deliberately-diverse reformulations of your query, each is fanned out across all sources, and the deduped union is re-ranked against your original query — surfacing relevant records a single keyword query would miss. Costs N× the upstream calls (bounded). Requires an LLM endpoint (LLM_API_BASE); with none configured the search runs as a normal single query and notes it in errors['multi_query']. The variants used are echoed in query_expansion. Composes with understand=. NOTE: multi_query=true ALWAYS applies semantic re-ranking of the window internally regardless of rank=; the rank= param has no effect in this mode.
published_afterNoKeep results with year >= this.
collapse_mirrorsNoOpt into conservative cross-repo content dedup (default false). On top of the always-on exact-DOI dedup, folds records that are the SAME dataset deposited under different (or no) DOIs — e.g. a Zenodo mirror of a figshare deposit, GEO<->ArrayExpress — into one record, annotating the survivor with the folded copies under mirrors[]. Conservative: a merge needs a shared file checksum OR identical (normalized-title, first-author-surname, year); title-only or partial matches never merge. Intra-page / best-effort only (a mirror on a different page is not collapsed), so a page may return fewer than size items; pagination is unaffected.
published_beforeNoKeep results with year <= this.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
queryYes
totalYes
errorsNo
resultsNo
next_cursorNo
mesh_expansionNo
assay_expansionNo
query_expansionNo
taxon_expansionNo
provenance_crateNo
tissue_expansionNo
chemical_expansionNo
query_understandingNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description fully discloses the fan-out behavior, per-source error reporting, and expansion mechanisms. It aligns with the readOnlyHint annotation. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough but not overly long; every sentence adds value. It is front-loaded with the main purpose and then provides detailed parameter explanations. Could be slightly more structured with bullet points, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 17 parameters, high complexity, and an output schema, the description covers the return format, error handling, pagination, and parameter interactions. It is complete for an AI agent to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds rich context for parameters like organism, disease, tissue, chemical, assay, multi_query, understand, and collapse_mirrors, explaining how expansions and deduplication work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Search public research-data archives...' and enumerates a comprehensive list of sources, making the tool's purpose highly specific. It clearly distinguishes from siblings like fetch, resolve, and list_sources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use resolve and fetch for more details, and when to use parameters like organism, disease, etc. It provides clear guidance on options like multi_query and understand, telling the agent when they are applicable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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