# NIH Reporter Intelligence MCP — $45B Federal Research (`andrew_avina/nih-reporter-mcp`) Actor Search, filter, and analyze NIH research grant awards from the NIH RePORTER v2 database via a Model Context Protocol (MCP) server. Query $45B+ in annual NIH funding by disease area, PI, institution, agency, and award type. This Apify Actor exposes structured grant data to AI a... - **URL**: https://apify.com/andrew\_avina/nih-reporter-mcp.md - **Developed by:** [Andrew Avina](https://apify.com/andrew_avina) (community) - **Categories:** MCP servers, Business - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $3.00 / 1,000 result item returneds 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 ## NIH Reporter MCP **NIH Research Grant Awards & Project Funding via NIH RePORTER v2** Search $45B+ in annual NIH research grant funding via the NIH RePORTER v2 API. Returns detailed grant records including award amounts, principal investigators, institutions, study sections, agencies, and project abstracts — searchable by disease area, agency, activity code, PI name, and institution. ### Inputs | Field | Type | Default | Description | |-------|------|---------|-------------| | `disease_area` | string | _(all)_ | Disease area or research topic (e.g. `cancer`, `diabetes`, `Alzheimer`) | | `agency` | string | _(all)_ | NIH agency code (e.g. `NCI`, `NIMH`, `NHLBI`, `NIAID`) | | `activity_code` | string | _(all)_ | Grant activity code (e.g. `R01`, `R21`, `K08`, `P30`, `U01`) | | `fiscal_year` | integer | `2023` | Fiscal year of award | | `pi_name` | string | _(all)_ | Principal investigator last name | | `institution` | string | _(all)_ | Recipient institution name | | `limit` | integer | `10` | Max records to return (1–100) | ### Output Fields Each record contains 24 fields: | Field | Description | |-------|-------------| | `project_num` | NIH project number (e.g. R01CA123456) | | `project_title` | Project title | | `fiscal_year` | Fiscal year of award | | `award_amount` | Award amount in USD | | `total_cost` | Total project cost in USD | | `direct_cost` | Direct costs in USD | | `indirect_cost` | Indirect (F&A) costs in USD | | `activity_code` | Grant activity code (R01, K08, P30, etc.) | | `award_type` | Full award type name | | `pi_names` | Principal investigator name(s) | | `pi_profile_id` | NIH PI profile ID | | `organization` | Recipient institution name | | `org_city` | Institution city | | `org_state` | Institution state | | `org_country` | Institution country | | `department` | Department or division | | `program_officer` | NIH program officer name | | `agency` | NIH agency/IC code | | `agency_full` | Full agency name | | `project_start` | Project start date (YYYY-MM-DD) | | `project_end` | Project end date (YYYY-MM-DD) | | `study_section` | Study section code | | `study_section_name` | Study section full name | | `abstract` | Project abstract (up to 500 characters) | ### Data Source [NIH RePORTER v2 API](https://api.reporter.nih.gov/) — National Institutes of Health Research Portfolio Online Reporting Tools. Covers all NIH-funded grants since 1985, updated daily. ### Use Cases - **Grant writers** — Find funded projects in target disease areas for benchmarking - **Pharma competitive intelligence** — Track NIH-funded research by disease and institution - **Academic research** — Identify funding patterns, study sections, and program officers - **Policy analysis** — NIH funding allocation across agencies, activity codes, and disease areas - **Technology transfer** — Discover federally funded IP available for licensing # Actor input Schema ## `query` (type: `string`): Search keyword matched against project title and abstract text. Examples: 'cancer immunotherapy', 'CRISPR gene editing', 'machine learning electronic health records', 'Alzheimer tau protein'. ## `organization` (type: `string`): Filter by grantee institution name. Examples: 'Harvard University', 'Johns Hopkins University', 'Mayo Clinic', 'Stanford University', 'University of California'. ## `piLastName` (type: `string`): Principal Investigator last name. Use with piFirstName to narrow results when the name is common. ## `piFirstName` (type: `string`): Principal Investigator first name (optional, use with piLastName). ## `agency` (type: `string`): NIH institute or center code. NCI=National Cancer Institute, NHLBI=Heart/Lung/Blood, NIMH=Mental Health, NIA=Aging, NIAID=Allergy/Infectious Diseases, NINDS=Neurological Disorders, NIDDK=Diabetes/Digestive/Kidney, NIGMS=General Medical Sciences, NHGRI=Genome Research, NIEHS=Environmental Health. ## `awardType` (type: `string`): NIH activity code for the grant mechanism. R01=standard research grant (most common), R21=exploratory/developmental, R03=small grant, K99/R00=career transition, U01=cooperative agreement, P01=program project, T32=training grant. ## `fiscalYear` (type: `integer`): NIH fiscal year to search (Oct 1 – Sep 30). 2024=FY2024. Defaults to 2023+2024 combined when not specified. Range: 2000–2025. ## `minAmount` (type: `number`): Return only grants with award amount greater than or equal to this value. Example: 500000 for $500K+ grants. Applied as a post-filter. ## `limit` (type: `integer`): Maximum number of grant records to return. In MCP server mode this is ignored. In batch mode, max is 500. Results are sorted by award amount descending. ## `serveMcp` (type: `boolean`): When enabled, starts an MCP-compatible HTTP server on port 4321 instead of returning a batch dataset. Use this to connect Claude Desktop, Cursor, or any MCP client directly to the NIH RePORTER database. The actor runs continuously until stopped. ## Actor input object example ```json { "query": "", "organization": "", "piLastName": "", "piFirstName": "", "agency": "", "awardType": "", "fiscalYear": 0, "minAmount": 0, "limit": 25, "serveMcp": false } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("andrew_avina/nih-reporter-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("andrew_avina/nih-reporter-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 '{}' | apify call andrew_avina/nih-reporter-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=andrew_avina/nih-reporter-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "NIH Reporter Intelligence MCP — $45B Federal Research", "description": "Search, filter, and analyze NIH research grant awards from the NIH RePORTER v2 database via a Model Context Protocol (MCP) server. Query $45B+ in annual NIH funding by disease area, PI, institution, agency, and award type. This Apify Actor exposes structured grant data to AI a...", "version": "0.1", "x-build-id": "sm3ZnEQpVgMi0UD5e" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/andrew_avina~nih-reporter-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-andrew_avina-nih-reporter-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/andrew_avina~nih-reporter-mcp/runs": { "post": { "operationId": "runs-sync-andrew_avina-nih-reporter-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/andrew_avina~nih-reporter-mcp/run-sync": { "post": { "operationId": "run-sync-andrew_avina-nih-reporter-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": { "query": { "title": "Keyword Search (Title + Abstract)", "type": "string", "description": "Search keyword matched against project title and abstract text. Examples: 'cancer immunotherapy', 'CRISPR gene editing', 'machine learning electronic health records', 'Alzheimer tau protein'.", "default": "" }, "organization": { "title": "Institution / Organization", "type": "string", "description": "Filter by grantee institution name. Examples: 'Harvard University', 'Johns Hopkins University', 'Mayo Clinic', 'Stanford University', 'University of California'.", "default": "" }, "piLastName": { "title": "PI Last Name", "type": "string", "description": "Principal Investigator last name. Use with piFirstName to narrow results when the name is common.", "default": "" }, "piFirstName": { "title": "PI First Name", "type": "string", "description": "Principal Investigator first name (optional, use with piLastName).", "default": "" }, "agency": { "title": "NIH Agency / Institute", "enum": [ "", "NCI", "NHLBI", "NIAID", "NINDS", "NIMH", "NIDDK", "NIGMS", "NEI", "NIDCR", "NICHD", "NIA", "NIAAA", "NIDA", "NIMHD", "NIBIB", "NHGRI", "NIEHS", "NLM", "NCATS", "OD" ], "type": "string", "description": "NIH institute or center code. NCI=National Cancer Institute, NHLBI=Heart/Lung/Blood, NIMH=Mental Health, NIA=Aging, NIAID=Allergy/Infectious Diseases, NINDS=Neurological Disorders, NIDDK=Diabetes/Digestive/Kidney, NIGMS=General Medical Sciences, NHGRI=Genome Research, NIEHS=Environmental Health.", "default": "" }, "awardType": { "title": "Award / Activity Type", "enum": [ "", "R01", "R21", "R03", "R33", "R34", "R41", "R42", "R43", "R44", "K99", "K01", "K08", "K23", "K24", "U01", "U19", "U54", "P01", "P30", "P50", "T32", "F30", "F31", "F32" ], "type": "string", "description": "NIH activity code for the grant mechanism. R01=standard research grant (most common), R21=exploratory/developmental, R03=small grant, K99/R00=career transition, U01=cooperative agreement, P01=program project, T32=training grant.", "default": "" }, "fiscalYear": { "title": "Fiscal Year", "minimum": 0, "maximum": 2025, "type": "integer", "description": "NIH fiscal year to search (Oct 1 – Sep 30). 2024=FY2024. Defaults to 2023+2024 combined when not specified. Range: 2000–2025.", "default": 0 }, "minAmount": { "title": "Minimum Award Amount (USD)", "minimum": 0, "type": "number", "description": "Return only grants with award amount greater than or equal to this value. Example: 500000 for $500K+ grants. Applied as a post-filter.", "default": 0 }, "limit": { "title": "Result Limit", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of grant records to return. In MCP server mode this is ignored. In batch mode, max is 500. Results are sorted by award amount descending.", "default": 25 }, "serveMcp": { "title": "MCP Server Mode", "type": "boolean", "description": "When enabled, starts an MCP-compatible HTTP server on port 4321 instead of returning a batch dataset. Use this to connect Claude Desktop, Cursor, or any MCP client directly to the NIH RePORTER database. The actor runs continuously until stopped.", "default": false } } }, "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 } } } } } } } } } } ```