# PubMed NCBI Scraper - Biomedical Articles & MeSH (No Key) (`themineworks/pubmed-ncbi-scraper`) Actor Scrape 36M+ PubMed/NCBI biomedical articles: title, abstract, authors, journal, PMID, DOI, MeSH terms. No API key needed. Build literature reviews & AI training corpora. Works in Claude, ChatGPT & any MCP agent. - **URL**: https://apify.com/themineworks/pubmed-ncbi-scraper.md - **Developed by:** [The Mine Works](https://apify.com/themineworks) (community) - **Categories:** Developer tools, MCP servers, Business - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## PubMed / NCBI — Biomedical Literature Search Search the world's largest biomedical literature database from Apify. Access 36 million+ peer-reviewed articles from PubMed and MEDLINE — titles, abstracts, authors, journals, PMIDs, DOIs, and MeSH controlled vocabulary terms — without any API key. An optional free NCBI API key unlocks higher throughput. ### Why This Actor? PubMed is the definitive source of record for biomedical and life sciences research, maintained by the National Library of Medicine (NLM) at NIH. With **36 million+ articles** spanning decades of research across medicine, pharmacology, genomics, neuroscience, oncology, and every other life science domain, it is the first stop for: - **Pharma and biotech researchers** conducting competitive intelligence, target identification, or systematic literature reviews - **AI and machine learning teams** building training corpora, biomedical NLP models, or question-answering systems that require structured scientific text - **Systematic review authors** collecting all studies matching a clinical PICO question for meta-analysis - **Biotech investors and analysts** tracking publication volume, author networks, and research momentum in a therapeutic area - **Academic departments** monitoring publications from collaborating institutions or specific research groups This actor wraps the official **NCBI E-utilities API** (esearch + efetch endpoints at `eutils.ncbi.nlm.nih.gov`) and delivers clean structured JSON — one article per dataset row — with full MeSH term arrays for downstream semantic analysis. ### PubMed Query Syntax The actor supports PubMed's full advanced query language. Use field tags in brackets to target specific fields: | Tag | Field | Example | |---|---|---| | `[ti]` | Article title | `CRISPR[ti]` | | `[au]` | Author name | `Smith J[au]` | | `[ta]` | Journal abbreviation | `Nature[ta]` | | `[mh]` | MeSH heading | `Neoplasms[mh]` | | `[dp]` | Publication date | `2023:2024[dp]` | | `[pt]` | Publication type | `Review[pt]` | Combine with boolean operators: `GLP-1 receptor agonist[ti] AND diabetes[mh] AND 2022:2024[dp]` ### Inputs | Field | Type | Description | Default | |---|---|---|---| | `query` | string | PubMed search query with optional field tags | `GLP-1 receptor agonist diabetes` | | `dateFrom` | string | Published from date (YYYY/MM/DD) | `2020/01/01` | | `dateTo` | string | Published to date (YYYY/MM/DD) | — | | `ncbiApiKey` | string | Optional free NCBI key (lifts rate limit 3x to 10 req/sec) | — | | `maxResults` | integer | Maximum articles to return (1–10,000) | `100` | ### Output Format Each article is stored as one item in the Apify dataset: ```json { "pmid": "38234567", "title": "Efficacy of GLP-1 receptor agonists in type 2 diabetes: a systematic review", "abstract": "Background: GLP-1 receptor agonists have emerged as...", "authors": ["Smith Jane A", "Chen Robert B", "Patel Anita K"], "journal": "The Lancet Diabetes & Endocrinology", "issn": "2213-8587", "year": "2024", "doi": "10.1016/S2213-8587(24)00123-4", "mesh_terms": ["Glucagon-Like Peptide-1 Receptor", "Diabetes Mellitus, Type 2", "Hypoglycemic Agents"], "url": "https://pubmed.ncbi.nlm.nih.gov/38234567/", "scraped_at": "2024-11-15T09:22:11.000Z" } ```` A summary record is appended at the end with total article count and run timestamp. ### MeSH Terms Medical Subject Headings (MeSH) are NLM's controlled vocabulary for indexing biomedical literature. Every PubMed article is manually tagged with MeSH descriptors by NLM indexers. The `mesh_terms` array in each output record contains these structured tags, which are ideal for: - Semantic clustering of articles by disease area - Building ontology-aligned training data for biomedical NLP models - Identifying related concepts the author did not use in the title or abstract ### Pricing **First 25 results are free** on every Apify account — no charge until you exceed the free tier. After the free tier: **$4 per 1,000 articles** (Pay-Per-Event billing). A 1,000-article run costs $4.00. A 10,000-article run costs $40.00. You are charged only for articles actually delivered. ### Frequently Asked Questions **Q: Do I need an NCBI API key?** No. The E-utilities API is freely accessible without authentication. However, without a key you are limited to 3 requests per second. A free NCBI API key (available at ncbi.nlm.nih.gov/account/settings/) increases this to 10 requests per second, making large runs significantly faster. Enter your key in the `ncbiApiKey` input field. **Q: How current is the data?** PubMed is updated daily with new articles, corrections, and MeSH annotations. Articles typically appear in PubMed within days to weeks of publication, depending on the journal's submission practices. **Q: Can I retrieve full text?** The actor retrieves titles, abstracts, and metadata. Full text is not available via the E-utilities API for most articles — full text access depends on publisher agreements. For open-access articles, the DOI in the output can be used to retrieve full text via PubMed Central (PMC) or the publisher. **Q: What is the maximum number of articles I can retrieve?** The actor supports up to 10,000 articles per run. For larger literature sets, use date range filters (`dateFrom`/`dateTo`) or narrower query terms to partition your retrieval across multiple runs. **Q: Can I search by specific author or institution?** Yes. Use the `[au]` tag for author names (e.g. `Smith JA[au]`) and the `[ad]` affiliation tag for institutions (e.g. `Harvard[ad]`). Multiple authors or institutions can be combined with OR: `(Smith JA[au] OR Chen RB[au])`. **Q: How does the actor handle rate limits?** The actor automatically respects NCBI's rate limits by inserting delays between requests — 340ms without an API key (staying safely under 3 req/sec) and 110ms with an API key. Automatic retry with exponential backoff handles transient 429 and 5xx errors. **Q: Are preprints included in PubMed results?** PubMed indexes peer-reviewed articles from MEDLINE-indexed journals. Preprints on bioRxiv/medRxiv are generally not indexed in PubMed. Use the arXiv or bioRxiv scrapers for preprint coverage. ### Use in Claude, ChatGPT & any MCP agent This actor is also a **Model Context Protocol (MCP) server tool** — call it directly from Claude, ChatGPT, Cursor, Windsurf, or any MCP-compatible AI agent. The agent only pays for results delivered (same pay-per-result model). - Per-actor MCP endpoint: `https://mcp.apify.com/?tools=themineworks/pubmed-ncbi-scraper` - Full Mine Works MCP server (all tools): `https://the-mine-works-mcp.hatchable.site/api/mcp` ```js // Call this actor as a tool via apify-client (Node) import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('themineworks/pubmed-ncbi-scraper').call({ /* input from the table above */ }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` # Actor input Schema ## `query` (type: `string`): PubMed search query. Supports field tags: \[ti] title, \[au] author, \[ta] journal, \[mh] MeSH. E.g. GLP-1 receptor agonist\[ti] AND diabetes\[mh] ## `dateFrom` (type: `string`): Filter articles published from this date. ## `dateTo` (type: `string`): Filter articles published up to this date. ## `ncbiApiKey` (type: `string`): Free key from ncbi.nlm.nih.gov/account/settings/ lifts rate limit from 3 to 10 requests/sec. ## `maxResults` (type: `integer`): Maximum articles to return. ## Actor input object example ```json { "query": "GLP-1 receptor agonist diabetes", "dateFrom": "2020/01/01", "maxResults": 25 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "GLP-1 receptor agonist diabetes", "dateFrom": "2020/01/01", "maxResults": 25 }; // Run the Actor and wait for it to finish const run = await client.actor("themineworks/pubmed-ncbi-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "query": "GLP-1 receptor agonist diabetes", "dateFrom": "2020/01/01", "maxResults": 25, } # Run the Actor and wait for it to finish run = client.actor("themineworks/pubmed-ncbi-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "query": "GLP-1 receptor agonist diabetes", "dateFrom": "2020/01/01", "maxResults": 25 }' | apify call themineworks/pubmed-ncbi-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=themineworks/pubmed-ncbi-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PubMed NCBI Scraper - Biomedical Articles & MeSH (No Key)", "description": "Scrape 36M+ PubMed/NCBI biomedical articles: title, abstract, authors, journal, PMID, DOI, MeSH terms. No API key needed. Build literature reviews & AI training corpora. Works in Claude, ChatGPT & any MCP agent.", "version": "0.1", "x-build-id": "4UkQBhjuqFRzmY9I7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/themineworks~pubmed-ncbi-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-themineworks-pubmed-ncbi-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/themineworks~pubmed-ncbi-scraper/runs": { "post": { "operationId": "runs-sync-themineworks-pubmed-ncbi-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/themineworks~pubmed-ncbi-scraper/run-sync": { "post": { "operationId": "run-sync-themineworks-pubmed-ncbi-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "query": { "title": "Search query", "type": "string", "description": "PubMed search query. Supports field tags: [ti] title, [au] author, [ta] journal, [mh] MeSH. E.g. GLP-1 receptor agonist[ti] AND diabetes[mh]" }, "dateFrom": { "title": "Published from (YYYY/MM/DD)", "type": "string", "description": "Filter articles published from this date." }, "dateTo": { "title": "Published to (YYYY/MM/DD)", "type": "string", "description": "Filter articles published up to this date." }, "ncbiApiKey": { "title": "NCBI API key (optional)", "type": "string", "description": "Free key from ncbi.nlm.nih.gov/account/settings/ lifts rate limit from 3 to 10 requests/sec." }, "maxResults": { "title": "Max articles", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum articles to return.", "default": 100 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```