# Institution Intelligence MCP — AI Agent Academic Intelligence (`red.cars/institution-intelligence-mcp`) Actor AI agent access to research institution data — ROR lookup, h-index, research output metrics, institutional comparisons, and sector analysis via OpenAlex. - **URL**: https://apify.com/red.cars/institution-intelligence-mcp.md - **Developed by:** [AutomateLab](https://apify.com/red.cars) (community) - **Categories:** MCP servers, AI - **Stats:** 0 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $30.00 / 1,000 search institutions 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 ## Institution Intelligence MCP Research institution data for AI agents — ROR lookup, h-index, research output metrics, institutional comparisons, and sector analysis via OpenAlex. --- ### 1. Purpose Statement Institution Intelligence MCP is an MCP (Model Context Protocol) server that gives AI agents direct access to 120,000+ research institutions across OpenAlex — universities, government research agencies, hospitals, and corporate R&D labs. AI agents performing VC due diligence, academic benchmarking, talent sourcing, or institutional research query institution profiles, h-index metrics, research areas, and comparative analysis without requiring API keys. **Built for:** AI agents doing VC due diligence on university technology transfer, academic librarians benchmarking institutions, recruiters sourcing talent from top research universities, corporate development scouting research partnerships, and institutional researchers building comparative reports. --- ### 2. Quick Start Add to your MCP client: ```json { "mcpServers": { "institution-intelligence-mcp": { "url": "https://red-cars--institution-intelligence-mcp.apify.actor/mcp" } } } ```` AI agents can now search 120,000+ research institutions, retrieve h-index and citation metrics, compare institutional research output, map research areas by institution, and analyze research output by country or sector. *** ### Comparison See how Institution Intelligence MCP compares to manual research and commercial platforms: [COMPARISON.md](./COMPARISON.md) *** ### 3. When to Call This MCP Use Institution Intelligence MCP when you need to: - **Find top research institutions** — search by name, country, or type with h-index and publication counts - **Get institutional profiles** — detailed metrics for a specific institution (ROR lookup, h-index, i10-index, top fields) - **Compare institutions** — side-by-side comparison of 2-5 institutions for VC due diligence, benchmarking, or talent sourcing - **Map research areas** — top research fields per institution with publication counts and field scores - **Analyze research output** — aggregated metrics by country, sector, or research area *** ### 4. What Data Can You Access? | Data Type | Source | Example | |-----------|--------|---------| | Institution profiles | OpenAlex | Name, ROR, type, country, h-index, i10-index | | Research output | OpenAlex | Works count, citations, mean citedness | | Research areas | OpenAlex | Top fields per institution with field scores | | Institutional comparison | OpenAlex | Side-by-side metrics for 2-5 institutions | | Sector analysis | OpenAlex | Aggregated metrics by country or type | *** ### 5. Why Use Institution Intelligence MCP? **The problem:** Researching institutional research strength — h-index, publication counts, top research areas, and comparative metrics — requires navigating disconnected databases (OpenAlex, ROR, institutional websites, citation databases). For VC analysts, corporate development teams, academic librarians, and recruiters, this data is essential for partnership decisions, talent sourcing, and institutional benchmarking. Manual research takes days across OpenAlex, ROR, and proprietary citation databases. **The solution:** AI agents use Institution Intelligence MCP to get instant, structured institutional intelligence — institution profiles with h-index and research output metrics, side-by-side comparisons for due diligence, research area mapping, and sector analysis. This is the institutional intelligence layer for AI agents doing VC due diligence, academic benchmarking, and research strategy. *** ### 6. Tools #### search\_institutions Search research institutions by name, country, or type. Returns institutions ranked by research output with h-index, works count, and citation metrics. ``` search_institutions(query="Stanford University", country="US", maxResults=10, sortBy="h_index") ``` #### get\_institution\_details Get detailed metrics for a specific institution by ROR ID or exact name match. Returns full research profile with h-index, i10-index, citation metrics, and research areas. ``` get_institution_details(institutionId="https://ror.org/00f54p054") ``` #### compare\_institutions Compare 2-5 institutions side-by-side: works count, citations, h-index, i10-index, top research areas, and publication output with relative rankings. ``` compare_institutions(institutions=["Harvard University", "Stanford University", "MIT"], metrics=["works_count", "cited_by_count", "h_index"]) ``` #### get\_institution\_research\_areas Get top research areas/concepts for an institution, with publication counts and field scores per area. ``` get_institution_research_areas(institutionId="https://ror.org/00f54p054", maxAreas=15) ``` #### analyze\_research\_output Analyze research output by country, sector, or research area. Returns aggregated metrics for the given filter. ``` analyze_research_output(country="US", sector="education", maxResults=25) ``` *** ### 7. Pricing | Tool | Price | |------|-------| | search\_institutions | $0.03 | | get\_institution\_details | $0.02 | | compare\_institutions | $0.05 | | get\_institution\_research\_areas | $0.03 | | analyze\_research\_output | $0.05 | All prices in USD per tool call. No API keys required — OpenAlex is fully open. *** ### 8. Setup ``` 1. Clone or download this actor 2. Run: npm install 3. Start: npm start 4. Add to your MCP client using the URL above ``` No API keys required. OpenAlex's public API is freely accessible. # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("red.cars/institution-intelligence-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("red.cars/institution-intelligence-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 red.cars/institution-intelligence-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=red.cars/institution-intelligence-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Institution Intelligence MCP — AI Agent Academic Intelligence", "description": "AI agent access to research institution data — ROR lookup, h-index, research output metrics, institutional comparisons, and sector analysis via OpenAlex.", "version": "1.0", "x-build-id": "RWpBWbKdNzUgUf5of" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/red.cars~institution-intelligence-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-red.cars-institution-intelligence-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/red.cars~institution-intelligence-mcp/runs": { "post": { "operationId": "runs-sync-red.cars-institution-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/red.cars~institution-intelligence-mcp/run-sync": { "post": { "operationId": "run-sync-red.cars-institution-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 } } } } } } } } } } ```