🧬 PubMed NCBI Scraper: Biomedical Articles & MeSH (No Key)

Overview

PubMed NCBI Scraper pulls biomedical literature from the NCBI PubMed database, which indexes 36M+ articles across medicine, life sciences, and healthcare. Search by keyword or by PubMed field tags ([ti] title, [au] author, [ta] journal, [mh] MeSH) with optional publication date range. Get back PMID, DOI, title, abstract, authors, journal, ISSN, MeSH terms, and publication year for every matching article. No API key required, with an optional free NCBI key that raises the rate limit.

It's the fastest way to build a biomedical literature corpus, feed a clinical RAG system, or run automated literature reviews.

Reliability posture: blocked, empty, or failed searches are never charged. You only pay for an article record that was actually delivered.

✅ No API key required | ✅ Full abstracts + MeSH | ✅ 36M+ articles | ✅ MCP-ready for AI agents

Features

Field-tagged search. PubMed's native tag syntax ([ti], [au], [ta], [mh]) supported end to end. Date range. Filter by publication date (YYYY/MM/DD). Full abstracts. Complete abstract text on every record. MeSH terms. Standardised biomedical vocabulary tags for every article. Optional NCBI key. Free key lifts the rate limit from 3 to 10 requests per second.

How it works

The actor calls the official NCBI E-utilities API (ESearch, EFetch, ESummary), which is the source PubMed itself is built on. Your query is submitted via ESearch to get the matching PMIDs, then EFetch pulls the full XML record for each PMID and normalises it into a flat JSON row.

Runs work without any credentials against NCBI's public rate limit (3 requests per second). Supplying a free NCBI API key in ncbiApiKey lifts the limit to 10 requests per second, which makes large runs meaningfully faster.

🧾 Input configuration

{
  "query": "GLP-1 receptor agonist[ti] AND diabetes[mh]",
  "dateFrom": "2020/01/01",
  "dateTo": "2026/06/30",
  "maxResults": 500
}

📤 Output format

{
  "pmid": "42446258",
  "title": "Evaluation of insulin, leptin, ghrelin, and adiponectin levels in type 2 diabetic patients receiving combined metformin-sulfonylurea therapy.",
  "abstract": "Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder, which is commonly related to reduced insulin responsiveness and altered appetite-related hormones. In this study, we have evaluated how combined metformin and sulfonylurea therapy affects glycemic indicators and appetite hormones, with special focus on hormone ratios...",
  "authors": ["S M Hussein", "N K Zaidan"],
  "journal": "Biomeditsinskaia khimiia",
  "issn": "2310-6972",
  "year": "2026",
  "doi": "10.18097/PBMCE0047",
  "mesh_terms": [
    "Humans",
    "Diabetes Mellitus, Type 2",
    "Metformin",
    "Adiponectin",
    "Leptin",
    "Ghrelin",
    "Insulin",
    "Female",
    "Male",
    "Middle Aged",
    "Sulfonylurea Compounds",
    "Insulin Resistance",
    "Hypoglycemic Agents",
    "Blood Glucose",
    "Drug Therapy, Combination",
    "Adult",
    "Glycated Hemoglobin"
  ],
  "url": "https://pubmed.ncbi.nlm.nih.gov/42446258/",
  "scraped_at": "2026-07-15T04:22:06.073Z"
}

This is a genuine article record from a live run against the real PubMed database. Note: the actor's field is url, not article_url, and there is no separate publication_date field; year is the field actually returned.

Every article record contains these fields:

Field	Description
🆔 pmid	PubMed identifier
📄 title	Article title
📝 abstract	Full abstract text
👥 authors	Array of author names
📰 journal	Journal name
🔢 issn	Journal ISSN
📅 year	Publication year
🔗 doi	Digital Object Identifier
🏷️ mesh_terms	Array of MeSH (Medical Subject Headings) terms
🌐 url	Canonical PubMed URL
🕒 scraped_at	ISO timestamp of when the record was captured

💼 Common use cases

Systematic reviews & meta-analyses Pull every article on a topic in a date range and load into a review-management tool. Filter by MeSH term for reproducible search strategies.

Clinical RAG & AI assistants Feed biomedical abstracts into a retrieval-augmented generation system for a clinical decision-support tool. Build a specialty-specific corpus (cardiology, oncology, endocrinology) for an AI agent.

Pharma competitive intel Track publications on a molecule or mechanism across time. Monitor a competitor's key opinion leaders by author search.

Grant & academic research support Build reference lists for a grant application or a paper's introduction. Monitor a lab's or institution's publication output.

🚀 Getting started

1. Open the actor and enter a PubMed query. Use tags for precision (GLP-1 receptor agonist[ti] AND diabetes[mh]).
2. Optionally set dateFrom and dateTo (YYYY/MM/DD).
3. Set maxResults to control cost.
4. Optionally paste a free NCBI key in ncbiApiKey for higher rate limits.
5. Click Start. Records stream to the dataset as pages parse.

FAQ

Do I need an NCBI API key? No. Runs work keyless at NCBI's public rate limit. A free NCBI key (from ncbi.nlm.nih.gov/account/settings/) lifts the rate limit and makes large runs significantly faster.

What PubMed field tags are supported? All of them. Use [ti] title, [au] author, [ta] journal abbreviation, [mh] MeSH, [dp] publication date, and any others documented by PubMed. Combine with AND, OR, NOT and parentheses.

How much does it cost? Pay per article returned, pay as you go. No subscription, no monthly minimum.

Can I use it in an AI agent? Yes. It's exposed as an MCP tool. See below.

Use in Claude, ChatGPT & any MCP agent

https://mcp.apify.com/?tools=themineworks/pubmed-ncbi-scraper

Or call it programmatically with the Apify client:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('themineworks/pubmed-ncbi-scraper').call({
  query: 'GLP-1 receptor agonist[ti] AND diabetes[mh]',
  dateFrom: '2023/01/01',
  maxResults: 100,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

🛠️ Complete your biomedical research pipeline

Got the articles. Now widen the corpus:

• arXiv Paper Scraper: pull preprints on the same topic before they hit a journal.
• Crossref Scholarly Metadata: pull citation counts and journal metadata for any DOI.
• FDA Recalls Scraper: cross-reference drug or device safety history.

Typical flow: pubmed pulls the peer-reviewed evidence, arxiv adds preprints, fda-recalls checks safety signals.

Questions or need a custom field set? Reach out through the Apify profile.