# Academic Research MCP — Papers, DOIs & Citations (`saturday/academic-research-mcp`) Actor MCP server + scraper for AI research agents: search 400M+ papers across Crossref, OpenAlex & arXiv, fetch DOIs, and trace both references and forward citations - plus author metrics. Built for literature review. - **URL**: https://apify.com/saturday/academic-research-mcp.md - **Developed by:** [Josh Compton](https://apify.com/saturday) (community) - **Categories:** AI, Agents, Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 research tool calls This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Academic Research MCP — Paper Search, DOIs & Citations (arXiv + Crossref + OpenAlex) An **MCP server for AI agents** — and a normal Apify scraper — that turns scholarly metadata into agent-callable tools. Point Claude, Cursor, or any MCP client at it and your agent can run a full literature review: search published papers and the latest preprints, look up a paper by DOI, and trace citations **both directions** — across **Crossref** (160M+ works), **OpenAlex** (240M+ works), and **arXiv**. Built for the AI-agent era: clean JSON in, structured results out, no API keys to manage, no HTML scraping. ### Why this one Most research tools on the store hit a single source and only go one direction. This one **aggregates three stable scholarly APIs** behind a consistent tool set and gives an agent the **forward citation graph** (who cites a paper), not just its bibliography — so it can ask "what's the newest work building on this?" - **Zero selector rot.** Wraps first-party JSON/XML APIs, not scraped HTML — it stays working. - **No keys, no logins.** Crossref (CC-BY), OpenAlex (CC0), and arXiv are public, reuse-friendly APIs. - **Fair billing.** You're only charged when a tool returns a successful result. ### Tools (MCP mode) | Tool | What it does | |---|---| | `search_papers` | Search peer-reviewed papers across **Crossref**, **OpenAlex**, or **all** (merged + de-duplicated, ranked by citations). | | `search_preprints` | Search the latest **arXiv** preprints; optional category filter (`cs.AI`, `stat.ML`, …). | | `get_paper_by_doi` | Full metadata for one paper by DOI. | | `get_references` | The works a paper **cites** (its bibliography) — backward tracing. | | `get_citations` | The works that **cite** a paper (forward citation graph), most-cited first. | | `get_author_works` | An author's most-cited works + metrics (works count, total citations, h-index). | ### Connect (MCP client) With the actor running in Standby mode, connect your MCP client to: ```` https://--academic-research-mcp.apify.actor/mcp ```` Use the **streamable HTTP** transport with an `Authorization: Bearer ` header: ```json { "mcpServers": { "academic-research": { "url": "https://--academic-research-mcp.apify.actor/mcp", "headers": { "Authorization": "Bearer " } } } } ```` Then ask your agent things like *"Find the most-cited papers on retrieval-augmented generation since 2024, then show me what's cited the top one this year."* ### Use it as a normal scraper (no MCP) You can also just **Run** it with input and get a dataset back: ```json { "operation": "search_papers", "query": "diffusion models", "source": "all", "fromYear": 2024, "maxResults": 15 } ``` Other operations: `search_preprints` (+ `category`), `get_paper_by_doi` / `get_references` / `get_citations` (+ `doi`), `get_author_works` (+ `author`). ### Pricing Pay-per-event: **$0.004 per successful MCP tool call** (failed/empty calls are free), or **$0.00001 per result** in a normal dataset run. A typical 30-call literature-review session costs well under $0.20. ### Data sources & terms - [Crossref REST API](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) — public, no auth, metadata under CC-BY. - [OpenAlex API](https://docs.openalex.org/) — public, no auth, CC0 data. - [arXiv API](https://info.arxiv.org/help/api/) — public, no auth; results subject to arXiv's terms. Public, logged-out data only. No login-gated content, no anti-bot circumvention. Not a medical/clinical data source. ### Local development ```bash npm install npm run build ## MCP / Standby mode: APIFY_META_ORIGIN=STANDBY ACTOR_WEB_SERVER_PORT=8080 npm run dev ## Standard run (writes to local dataset): node dist/main.js ``` *** *Built with Claude Code. Wraps public scholarly APIs; not affiliated with Crossref, OpenAlex, or arXiv.* # Actor input Schema ## `operation` (type: `string`): What to do in a standard run. (Ignored in Standby/MCP mode.) ## `query` (type: `string`): Terms to search for (used by the search operations). ## `source` (type: `string`): Which database to search. ## `doi` (type: `string`): A DOI (for get\_paper\_by\_doi / get\_references / get\_citations), e.g. 10.1038/s41586-021-03819-2 ## `author` (type: `string`): Author full name (for get\_author\_works), e.g. Yoshua Bengio ## `category` (type: `string`): Optional arXiv category filter for search\_preprints, e.g. cs.AI, cs.LG, stat.ML ## `fromYear` (type: `integer`): Only return papers published in this year or later (search operations). ## `maxResults` (type: `integer`): Maximum number of results (1–25). ## `politeMailto` (type: `string`): Optional. Sent to Crossref/OpenAlex/arXiv as a courtesy for the faster 'polite pool'. Not stored beyond the upstream request. ## Actor input object example ```json { "operation": "search_papers", "query": "large language models", "source": "crossref", "maxResults": 10 } ``` # 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": "large language models" }; // Run the Actor and wait for it to finish const run = await client.actor("saturday/academic-research-mcp").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": "large language models" } # Run the Actor and wait for it to finish run = client.actor("saturday/academic-research-mcp").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": "large language models" }' | apify call saturday/academic-research-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saturday/academic-research-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Academic Research MCP — Papers, DOIs & Citations", "description": "MCP server + scraper for AI research agents: search 400M+ papers across Crossref, OpenAlex & arXiv, fetch DOIs, and trace both references and forward citations - plus author metrics. Built for literature review.", "version": "0.1", "x-build-id": "YqUovEXuo1GR65bRY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saturday~academic-research-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saturday-academic-research-mcp", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/saturday~academic-research-mcp/runs": { "post": { "operationId": "runs-sync-saturday-academic-research-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/saturday~academic-research-mcp/run-sync": { "post": { "operationId": "run-sync-saturday-academic-research-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "operation": { "title": "Operation", "enum": [ "search_papers", "search_preprints", "get_paper_by_doi", "get_references", "get_citations", "get_author_works" ], "type": "string", "description": "What to do in a standard run. (Ignored in Standby/MCP mode.)", "default": "search_papers" }, "query": { "title": "Search query", "type": "string", "description": "Terms to search for (used by the search operations)." }, "source": { "title": "Source (for Search papers)", "enum": [ "crossref", "openalex", "all" ], "type": "string", "description": "Which database to search.", "default": "crossref" }, "doi": { "title": "DOI", "type": "string", "description": "A DOI (for get_paper_by_doi / get_references / get_citations), e.g. 10.1038/s41586-021-03819-2" }, "author": { "title": "Author name", "type": "string", "description": "Author full name (for get_author_works), e.g. Yoshua Bengio" }, "category": { "title": "arXiv category", "type": "string", "description": "Optional arXiv category filter for search_preprints, e.g. cs.AI, cs.LG, stat.ML" }, "fromYear": { "title": "From year", "minimum": 1900, "maximum": 2100, "type": "integer", "description": "Only return papers published in this year or later (search operations)." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 25, "type": "integer", "description": "Maximum number of results (1–25).", "default": 10 }, "politeMailto": { "title": "Contact email (API polite pool)", "type": "string", "description": "Optional. Sent to Crossref/OpenAlex/arXiv as a courtesy for the faster 'polite pool'. Not stored beyond the upstream request." } } }, "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 } } } } } } } } } } ```