Data Aggregator MCP vs Infranodus MCP Server… | AllMCPs
Side-by-Side Model Context Protocol Comparison
Data Aggregator MCP vs Infranodus MCP Server Infranodus
In-depth architectural comparison of the Data Aggregator MCP and Infranodus MCP Server Infranodus MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Data Aggregator MCP
Research · Local stdio
Quality: 57/100 (Good) | Auth: No auth required
Infranodus MCP Server Infranodus
Research · Local stdio
Quality: 68/100 (Great) | Auth: API Key required
Verdict Summary: Choose Data Aggregator MCP if you need specialized Research tools running via a local process. Choose Infranodus MCP Server Infranodus if your workspace requires Research integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Data Aggregator MCP when:
You need dedicated capabilities in the Research domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Search and fetch research datasets across Zenodo, DataCite (Dryad/Figshare/Dataverse/OSF), NCBI omics (GEO/SRA/BioProject), and literature (PubMed/OpenAIRE) behind one normalized model — DOI deduplication, NCBI-Taxonomy synonym expansion, paper→data linking, and checksum-verified download. uvx data-aggregator-mcp.
Map text into knowledge graphs to create a structured representation of conceptual relations and t…
Category & Scope
Tools & Capabilities Breakdown
Data Aggregator MCP Tools (6)
search
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=<name> to expand the query with NCBI-Taxonomy synonyms; results carry normalized taxa[] + plant cross-links. Pass disease=<name> 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=<name> to expand the query with UBERON synonyms (e.g. 'liver' also matches 'iecur'/'jecur'); the expansion is echoed in tissue_expansion. Pass chemical=<name> 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=<name> 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[]. An ontology param that matches no term in its registry (e.g. organism='yeast' — NCBI Taxonomy indexes no such common name) is reported in unresolved[] and the search runs WITHOUT that expansion, so a dropped filter is never silent. Clients that support form elicitation are asked for a replacement term before the search runs.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Data Aggregator MCP is categorized under Research and uses a local stdio subprocess. In contrast, Infranodus MCP Server Infranodus belongs to Research using local stdio subprocess. Select Data Aggregator MCP when you need capabilities focused on research and Infranodus MCP Server Infranodus when you require tools for research.
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=<format> 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=<intent> (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.
fetch
Download a resource's files to local disk and return the PATHS (never the file contents). 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 + MetaboLights only (unverified), MassIVE/GNPS/PeptideAtlas/Metabolomics Workbench fail loud; DANDI dandisets (302→S3, unverified); CZ CELLxGENE H5AD/RDS assets (unverified); OpenML ARFF (md5-verified); RCSB PDB .cif/.pdb structure files (unverified). Fails loud if selected files exceed max_bytes unless force=true. Verifies checksums; writes a .dataresource.json sidecar.
list_sources
List wired data sources and their capabilities (layer, kinds, supported filters, auth requirement, rate limit, status).
operate
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). 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.
relate
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. 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.
Infranodus MCP Server Infranodus Tools (40)
generate_knowledge_graph
Generate a knowledge graph with main topics, topical clusters, concepts, concepts (nodes) relations (edges) and structural gaps. Only use when explicitly asked to analyze a text or generate a knowledge graph. Do not use for short clarifying questions that you already have an answer to from the context of the conversation.
create_knowledge_graph
Create a knowledge graph in InfraNodus from text or from a URL, save it, and provide its name and a link to it for future use.
generate_ontology_graph
Use AI to generate a reasoning ontology knowledge graph (entities and the relations between them) and optionally save it as a InfraNodus graph. Three sources, provide exactly one: prompt (a topic — one AI call), text (a long document or a structural digest of a project, chunked server-side), or sourceGraphName (an existing graph — e.g. a fully ingested repo, vault, or corpus — whose statements are read back, chunked, and condensed into an ontology). Set ontologyMode: 'codebase' for software projects, or 'procedural' to write a DIGEST of how the project works (prose statements with [[wikilinks]], not relation triples) from an already-uploaded graph — save it as <repo|vault>-<project>-digest for optimize_knowledge_base. Use to get a rich overview, a reasoning map of a topic, or a condensed 'how it fits together' graph of a large corpus.
memory_add_relations
Add relations to the InfraNodus memory from text, save it, and provide its name and a link to it for future use.
memory_get_relations
Provide a list of relations from the InfraNodus memory for a given concept or entity
analyze_existing_graph_by_name
Extract and analyze the content of an existing InfraNodus graph from your account.
analyze_text
Extract and analyze a graph from text, URL, YouTube video transcript, or an existing InfraNodus graph.
generate_content_gaps
Generate content gaps from text, URL, or an existing graph using knowledge graph analysis.
generate_topical_clusters
Generate topics and clusters of keywords from text, URL, or an existing graph using knowledge graph analysis.
generate_research_questions
Analyze text or an existing graph and generate innovative research questions based on the content gaps identified between the topical clusters. Provide either text, url, or graphName. Can be used to improve the text and the discourse it relates to
generate_research_ideas
Analyze text or an existing graph and generate innovative research ideas based on the content gaps identified between the topical clusters inside the text that can be used to improve the text and the discourse it relates to.
generate_responses_from_graph
Use text, URL, or an existing InfraNodus knowledge graph and generate responses and expert advice based on a prompt provided.