Data Aggregator

Name: Data Aggregator
Author: musharna

by musharnaversion 0.75.4i★ –iRuns on your computer

Registry nameio.github.musharna/data-aggregator-mcp

Install

Overview

Description from the maker (original English)

Find & fetch research datasets across 21 archives, omics registries, and literature sources.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: NCBI_API_KEY, LLM_API_KEY, EMBEDDING_API_KEY, DATA_GOV_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: NCBI_API_KEY, LLM_API_BASE, LLM_API_KEY, LLM_MODEL, EMBEDDING_API_BASE, EMBEDDING_API_KEY, EMBEDDING_MODEL, DATA_GOV_API_KEY, UNPAYWALL_EMAIL, NCBI_EMAIL, DATAVERSE_BASE_URL, CACHE_TTL_SECONDS

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "data-aggregator": {
      "command": "uvx",
      "args": [
        "data-aggregator-mcp"
      ],
      "env": {
        "NCBI_API_KEY": "your-value",
        "LLM_API_BASE": "your-value",
        "LLM_API_KEY": "your-value",
        "LLM_MODEL": "your-value",
        "EMBEDDING_API_BASE": "your-value",
        "EMBEDDING_API_KEY": "your-value",
        "EMBEDDING_MODEL": "your-value",
        "DATA_GOV_API_KEY": "your-value",
        "UNPAYWALL_EMAIL": "your-value",
        "NCBI_EMAIL": "your-value",
        "DATAVERSE_BASE_URL": "your-value",
        "CACHE_TTL_SECONDS": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add data-aggregator --env NCBI_API_KEY=your-value --env LLM_API_BASE=your-value --env LLM_API_KEY=your-value --env LLM_MODEL=your-value --env EMBEDDING_API_BASE=your-value --env EMBEDDING_API_KEY=your-value --env EMBEDDING_MODEL=your-value --env DATA_GOV_API_KEY=your-value --env UNPAYWALL_EMAIL=your-value --env NCBI_EMAIL=your-value --env DATAVERSE_BASE_URL=your-value --env CACHE_TTL_SECONDS=your-value -- uvx data-aggregator-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "data-aggregator": {
      "command": "uvx",
      "args": [
        "data-aggregator-mcp"
      ],
      "env": {
        "NCBI_API_KEY": "your-value",
        "LLM_API_BASE": "your-value",
        "LLM_API_KEY": "your-value",
        "LLM_MODEL": "your-value",
        "EMBEDDING_API_BASE": "your-value",
        "EMBEDDING_API_KEY": "your-value",
        "EMBEDDING_MODEL": "your-value",
        "DATA_GOV_API_KEY": "your-value",
        "UNPAYWALL_EMAIL": "your-value",
        "NCBI_EMAIL": "your-value",
        "DATAVERSE_BASE_URL": "your-value",
        "CACHE_TTL_SECONDS": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "data-aggregator": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "data-aggregator-mcp"
      ],
      "env": {
        "NCBI_API_KEY": "your-value",
        "LLM_API_BASE": "your-value",
        "LLM_API_KEY": "your-value",
        "LLM_MODEL": "your-value",
        "EMBEDDING_API_BASE": "your-value",
        "EMBEDDING_API_KEY": "your-value",
        "EMBEDDING_MODEL": "your-value",
        "DATA_GOV_API_KEY": "your-value",
        "UNPAYWALL_EMAIL": "your-value",
        "NCBI_EMAIL": "your-value",
        "DATAVERSE_BASE_URL": "your-value",
        "CACHE_TTL_SECONDS": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

NCBI_API_KEY secret env
Optional NCBI E-utilities API key; raises the rate limit 3->10 req/s for omics, literature, and taxonomy lookups.
LLM_API_BASE env
Base URL of an OpenAI-compatible /chat/completions endpoint; enables search(understand=true) and search(multi_query=true).
LLM_API_KEY secret env
Bearer token for the LLM endpoint (LLM_API_BASE); omit for keyless local servers.
LLM_MODEL env
Model name passed to the LLM endpoint (default: gpt-4o-mini); set to whatever your endpoint serves.
EMBEDDING_API_BASE env
Base URL of an OpenAI-compatible /embeddings endpoint; enables search(rank=semantic) re-ranking.
EMBEDDING_API_KEY secret env
Bearer token for the embeddings endpoint (EMBEDDING_API_BASE); omit for keyless local servers.
EMBEDDING_MODEL env
Model name passed to the embeddings endpoint (default: text-embedding-3-small).
DATA_GOV_API_KEY secret env
Optional api.data.gov key for the data.gov source; without it requests fall back to the shared public DEMO_KEY, which is aggressively rate-limited.
UNPAYWALL_EMAIL env
Email address sent to the Unpaywall API; required to enable the Unpaywall PDF fallback leg of literature full-text retrieval.
NCBI_EMAIL env
Contact address sent to NCBI's ID converter; falls back to UNPAYWALL_EMAIL when unset.
DATAVERSE_BASE_URL env
Resolve Dataverse DOIs against a different installation (default: https://dataverse.harvard.edu).
CACHE_TTL_SECONDS env
Resolve-cache lifetime in seconds (default: 3600); an unparseable value falls back to the default.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account musharna, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5653 servers in this category
Published to the registry more recently than 69% of the other 5653 servers in this category
Basic checks equal to or better than 100% of the other 5653 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Data Aggregator safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: NCBI_API_KEY, LLM_API_KEY, EMBEDDING_API_KEY, DATA_GOV_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.75.4) reached the registry 18 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

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Codebase Memory

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Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

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Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.

Install

Claude Code:

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

One-click install for VS Code or Cursor:

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

Claude Desktop, pip, and the operate extra

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]".

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

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.

Data Aggregator

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

data-aggregator-mcp

Install

What you can ask

How a search runs

What the benchmark shows

Compared with other servers

Reference and development

License