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musharna

data-aggregator-mcp

resolve

Read-only

Fetch complete metadata and file manifest for any research dataset by its DOI, Zenodo ID, or other identifier. Optionally attach citation, retraction status, FAIR score, and license compatibility.

Instructions

Fetch the full DataResource for a known id (e.g. 'zenodo:7654321', 'datacite:10.5061/dryad.x', 'hf:owner/name', 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesSource-prefixed id, bare Zenodo id, or DOI
citeNoOptional citation format to render onto the result: 'bibtex', 'ris', 'csl-json', or any CSL style name ('apa', 'mla', 'vancouver', ...). DOI-bearing records render via DOI content negotiation; non-DOI records support 'csl-json' only. Omitted = no citation. Failures degrade quietly (citation stays null).
formatNoOptional export to render onto the result. 'croissant' attaches a file-level Croissant JSON-LD manifest (croissant field); 'ro-crate' attaches a minimal RO-Crate 1.1 manifest (ro_crate field); 'provenance' attaches a one-call RO-Crate 1.1 data-availability dossier (provenance field) bundling version-currency, licence+SPDX, FAIR score, retraction status, and the source/DOI/ID chain — it auto-attaches fair{} and trust{} so the dossier is complete in one call (unknown signals are reported as unknown, never as a clean claim).
trustNoWhen true, attach trust signals (retraction status via Crossref) to the result under trust{}. One extra Crossref call; only meaningful for DOI-bearing records (a DataCite data DOI Crossref does not register leaves retracted=null = unknown, never a false clean claim).
fairNoWhen true, attach an RDA-grounded FAIRness assessment under fair{}: a 0–100 overall score plus findable/accessible/interoperable/reusable sub-scores, the count of indicators evaluated, and actionable gaps each naming its RDA FAIR Data Maturity Model indicator id. Pure/local — no network call. Only the machine-evaluable subset is scored (never fabricates what the metadata cannot show).
useNoWhen set, attach a licence-compatibility advisory under license_compat{} for an intended use of the record. Supported intents: 'commercial', 'redistribute', 'modify', 'ml-training' (training = a derivative+commercial use, our stated interpretation). The verdict is ALLOW/REVIEW/DENY computed from a bundled choosealicense.com licence matrix keyed on the normalized SPDX id, naming the governing clause — a metadata-derived advisory, NOT legal advice. An unrecognized or absent licence yields REVIEW (never a fabricated ALLOW/DENY); an unknown intent is an error.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
sourceYes
kindYes
titleYes
creatorsNo
fundingNo
yearNo
descriptionNo
doiNo
identifiersNo
accessionsNo
organismNo
taxaNo
subjectsNo
licenseNo
accessNo
filesNo
linksNo
citationNo
metricsNo
trustNo
fairNo
license_compatNo
is_latestNo
superseded_byNo
last_updatedNo
croissantNo
ro_crateNo
provenanceNo
access_modesNo
mirrorsNo
Behavior5/5

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

Annotations already indicate readOnlyHint=true, and the description reinforces this as a read-only fetch. It goes beyond annotations by detailing side effects: network calls for trust, pure/computation for fair, quiet degradation for citation failures, and explicit handling of unknown signals. No contradictions with annotations, and transparency is thorough.

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 a single paragraph that efficiently covers main purpose and all optional parameters without redundancy. It is front-loaded with the core function. However, it is somewhat dense and could be improved with bullet points or clearer separation of concerns, but overall remains concise. Score 4.

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?

Given the tool has 6 parameters with 100% schema coverage, an output schema (not shown but implied), and an anntoation for read-only, the description fills all remaining gaps: error handling, constraints (trust only for DOI), behavioral edge cases (unknown signals), and parameter interactions. It is comprehensively complete for the agent to use correctly. Score 5.

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

Parameters4/5

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

All 6 parameters have descriptions in the input schema (100% coverage). The description adds value by explaining behavioral details not in schema: e.g., 'failures degrade quietly' for cite, 'unknown signals are reported as unknown' for provenance, 'pure/local — no network call' for fair. This surpasses baseline, hence 4.

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 begins with a clear verb+resource combination: 'Fetch the full DataResource for a known id', immediately stating what the tool does. It lists specific id formats (zenodo, datacite, hf, bare ID, DOI) and distinguishes from siblings like 'search' implicitly by focusing on known IDs. This specificity and differentiation justify a score of 5.

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

Usage Guidelines4/5

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

The description provides explicit context for each optional parameter (cite, trust, fair, use, format) indicating when they are meaningful (e.g., trust only for DOI-bearing records) and expected behavior (e.g., failures degrade quietly). It does not, however, explicitly contrast with sibling tools like 'search' for when not to use resolve. This leaves mild ambiguity, so score is 4.

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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