# PubMed Scraper API - Articles, Abstracts & Authors (`pink_comic/pubmed-article-search`) Actor Search official NCBI PubMed E-utilities by query, title, author, journal, date, type, and language. Returns bounded source-linked biomedical bibliography records and optional abstracts with explicit evidence limits. - **URL**: https://apify.com/pink\_comic/pubmed-article-search.md - **Developed by:** [Ava Torres](https://apify.com/pink_comic) (community) - **Categories:** Other, Education, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 pubmed article search scraper api evidence items 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Article Search Scraper API Search biomedical literature through the official **NCBI PubMed E-utilities**. Use native PubMed query syntax or structured title, author, journal, publication-date, article-type, and language filters. Results include source-linked bibliography records; optional abstract fetching uses NCBI EFetch. ### Fast first run ```json {"query":"artificial intelligence[Title]","sort":"newest","includeAbstract":false,"maxResults":5} ```` The deterministic default returns up to five recent records and costs at most **$0.0101** (`$0.0001` start + `$0.002` per dataset item). ### Output Found rows include `pmid`, title, stable `authors`, journal/full journal name, publication and e-publication dates, DOI and PMC identifiers, stable identifier/publication-type/language arrays, exact `pubmedUrl`, query/sort/result-position metadata, source retrieval time, bounded-query disclosure, and mandatory evidence limits. `abstract` is populated only when `includeAbstract=true` and source abstract text is available. `NO_MATCH`, `INVALID_INPUT`, and `SOURCE_UNAVAILABLE` produce explicit evidence rows rather than silent empty datasets, unless a buyer charge cap permits zero items. ### Examples Structured review search: ```json { "title": "machine learning", "journal": "Nature Medicine", "publicationDateFrom": "2025/01/01", "publicationDateTo": "2026/12/31", "articleType": "Review", "language": "English", "sort": "newest", "includeAbstract": true, "maxResults": 25 } ``` Native syntax: ```json {"query":"(diabetes[Title/Abstract]) AND randomized controlled trial[Publication Type]","sort":"relevance","maxResults":10} ``` `maxResults` is a total 1–500 item cap, not a per-page cap. Apify's Maximum total charge further reduces the cap before source enrichment. ### Evidence boundaries - PubMed is an indexed bibliographic database; inclusion is **not NCBI endorsement**. - A record does not prove peer review, article quality, clinical validity, applicability, or consensus. - Results are not clinical guidance or medical advice. - A bounded query is not complete literature coverage. Search syntax, indexing status, filters, and source updates affect recall. - Publication dates, identifiers, abstracts, and other metadata can be corrected or updated. - Abstract availability varies and an empty abstract does not establish that the article has no abstract elsewhere. Always review the linked PubMed record and underlying publication before relying on a result. ### Reliability and NCBI usage The actor identifies itself with a descriptive tool/email User-Agent, paces no-key requests below NCBI's general three-requests-per-second guidance, batches PMIDs (up to 100/request), and bounds pagination. It retries transport errors, body-read failures, HTTP 408/429/5xx responses (including `Retry-After`), and malformed JSON/XML responses. It never substitutes unofficial mirrors when NCBI is unavailable. ### Pricing | Component | Price | |---|---:| | Actor start | `$0.0001` | | Dataset item | `$0.002` | Five items cost at most `$0.0101`; 25 cost at most `$0.0501`. # Actor input Schema ## `query` (type: `string`): Complete PubMed query syntax. Combined with structured filters using AND. ## `title` (type: `string`): Optional title term filter or result-control setting. See the README for source semantics and examples. ## `author` (type: `string`): Optional author term filter or result-control setting. See the README for source semantics and examples. ## `journal` (type: `string`): Optional journal term filter or result-control setting. See the README for source semantics and examples. ## `publicationDateFrom` (type: `string`): YYYY/MM/DD ## `publicationDateTo` (type: `string`): YYYY/MM/DD ## `articleType` (type: `string`): PubMed publication type, such as Review or Clinical Trial ## `language` (type: `string`): PubMed language value, such as English ## `sort` (type: `string`): Optional sort filter or result-control setting. See the README for source semantics and examples. ## `includeAbstract` (type: `boolean`): Fetch available abstracts through a bounded NCBI EFetch XML request. ## `maxResults` (type: `integer`): Total paid item cap. Default five-item run costs at most $0.0101 including start. ## Actor input object example ```json { "query": "artificial intelligence[Title]", "title": "", "author": "", "journal": "", "publicationDateFrom": "", "publicationDateTo": "", "articleType": "", "language": "", "sort": "newest", "includeAbstract": false, "maxResults": 5 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "artificial intelligence[Title]", "maxResults": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("pink_comic/pubmed-article-search").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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": "artificial intelligence[Title]", "maxResults": 5, } # Run the Actor and wait for it to finish run = client.actor("pink_comic/pubmed-article-search").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": "artificial intelligence[Title]", "maxResults": 5 }' | apify call pink_comic/pubmed-article-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=pink_comic/pubmed-article-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PubMed Scraper API - Articles, Abstracts & Authors", "description": "Search official NCBI PubMed E-utilities by query, title, author, journal, date, type, and language. Returns bounded source-linked biomedical bibliography records and optional abstracts with explicit evidence limits.", "version": "0.1", "x-build-id": "YTqCQIoweQcXKnqCT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/pink_comic~pubmed-article-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-pink_comic-pubmed-article-search", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/pink_comic~pubmed-article-search/runs": { "post": { "operationId": "runs-sync-pink_comic-pubmed-article-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/pink_comic~pubmed-article-search/run-sync": { "post": { "operationId": "run-sync-pink_comic-pubmed-article-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "PubMed Query", "type": "string", "description": "Complete PubMed query syntax. Combined with structured filters using AND.", "default": "artificial intelligence[Title]" }, "title": { "title": "Title Term", "type": "string", "description": "Optional title term filter or result-control setting. See the README for source semantics and examples.", "default": "" }, "author": { "title": "Author Term", "type": "string", "description": "Optional author term filter or result-control setting. See the README for source semantics and examples.", "default": "" }, "journal": { "title": "Journal Term", "type": "string", "description": "Optional journal term filter or result-control setting. See the README for source semantics and examples.", "default": "" }, "publicationDateFrom": { "title": "Publication Date From", "type": "string", "description": "YYYY/MM/DD", "default": "" }, "publicationDateTo": { "title": "Publication Date To", "type": "string", "description": "YYYY/MM/DD", "default": "" }, "articleType": { "title": "Article Type", "type": "string", "description": "PubMed publication type, such as Review or Clinical Trial", "default": "" }, "language": { "title": "Language", "type": "string", "description": "PubMed language value, such as English", "default": "" }, "sort": { "title": "Sort", "enum": [ "newest", "relevance", "first author", "journal" ], "type": "string", "description": "Optional sort filter or result-control setting. See the README for source semantics and examples.", "default": "newest" }, "includeAbstract": { "title": "Include Abstract", "type": "boolean", "description": "Fetch available abstracts through a bounded NCBI EFetch XML request.", "default": false }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Total paid item cap. Default five-item run costs at most $0.0101 including start.", "default": 5 } } }, "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 } } } } } } } } } } ```