# Academic Research MCP — AI Agent Research Librarian (`red.cars/academic-research-mcp`) Actor

Search 600M+ academic papers, grants, and citations for AI agents. One tool call to CrossRef, OpenAlex, Semantic Scholar, DBLP, CORE, PubMed, NIH, and NSF.

- **URL**: https://apify.com/red.cars/academic-research-mcp.md
- **Developed by:** [AutomateLab](https://apify.com/red.cars) (community)
- **Categories:** Developer tools, Other
- **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet

## Pricing

from $20.00 / 1,000 search papers

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 Server

> Search 600M+ academic papers, grants, and citations for AI agents.

**[View on Apify](https://apify.com/wdavalos/academic-research-mcp)** | **[MCP Endpoint](https://academic-research-mcp.apify.actor/mcp)**

---

### What It Does

Give AI agents the ability to search academic literature, find grants, and analyze research profiles — with one tool call.

- **600M+ papers** searchable across CrossRef, OpenAlex, Semantic Scholar, DBLP, CORE, PubMed
- **NIH + NSF grants** searchable by topic
- **Institution + author profiles** with h-index and publication stats
- **Citation analysis** — find who cited a paper
- **Research trends** — track topic popularity over time
- **Systematic reviews** — deduplicated, citation-ranked results

---

### Quick Start

Add to your AI agent:

```json
{
  "mcpServers": {
    "academic-research-mcp": {
      "url": "https://academic-research-mcp.apify.actor/mcp"
    }
  }
}
````

Or with authentication:

```json
{
  "mcpServers": {
    "academic-research-mcp": {
      "url": "https://academic-research-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN"
    }
  }
}
```

***

### Tools

| Tool | Price | Description |
|------|-------|-------------|
| `search_papers` | $0.02 | Search papers across all databases |
| `get_paper_details` | $0.01 | Get metadata by DOI |
| `find_citations` | $0.02 | Find papers citing a given paper |
| `find_grants` | $0.03 | Search NIH, NSF, foundation grants |
| `institution_research_profile` | $0.05 | Institution h-index, stats, topics |
| `author_research_profile` | $0.03 | Author h-index, top papers, co-authors |
| `research_trends` | $0.05 | Topic trends over time |
| `systematic_review` | $0.10 | Full literature review across all DBs |

***

### Example Calls

#### Search Papers

```
search_papers(query="transformer attention mechanism", max_results=10)
```

Returns:

```json
{
  "title": "Attention Is All You Need",
  "authors": ["Vaswani", "Shazeer", "Parmar", ...],
  "year": 2017,
  "doi": "10.48550/arXiv.1706.03762",
  "journal": "NeurIPS",
  "citations": 98000,
  "source": "CrossRef"
}
```

#### Find Grants

```
find_grants(query="machine learning NLP", funder_type="all")
```

Returns:

```json
{
  "title": "Neural Network Interpretability for NLP",
  "agency": "NSF",
  "award_id": "NSF-2024-12345",
  "amount": 500000,
  "pi": "Dr. Jane Smith",
  "institution": "MIT",
  "deadline": "2024-05-15"
}
```

#### Institution Profile

```
institution_research_profile(institution_name="Stanford University")
```

Returns:

```json
{
  "name": "Stanford University",
  "country": "US",
  "paper_count": 215000,
  "citation_count": 8900000,
  "h_index": 892,
  "topics": ["machine learning", "AI", "NLP", ...]
}
```

***

### How It Works

**Phase 1: Query Parsing**

- Receives tool call with query parameters
- Validates input schema

**Phase 2: Multi-Source Search**

- Queries CrossRef (150M papers)
- Queries OpenAlex (250M papers)
- Queries Semantic Scholar (200M papers)
- Queries NIH RePORTER (grants)
- Queries NSF Award API (grants)
- All queries run in parallel

**Phase 3: Aggregation**

- Deduplicates results by DOI
- Sorts by citation count
- Returns structured JSON

***

### Data Sources

| Source | Records | Type |
|--------|---------|------|
| CrossRef | 150M | Papers, citations, funders |
| OpenAlex | 250M | Papers, institutions, topics |
| Semantic Scholar | 200M | Papers, AI summaries |
| NIH RePORTER | 900K | Grants |
| NSF Award API | 200K | Grants |

***

### Use Cases

#### Literature Review

*"Find papers on transformer models for time series forecasting"*
→ AI calls `search_papers` → Returns ranked papers with citations, abstracts, DOIs

#### Grant Discovery

*"What grants exist for NLP research under $1M?"*
→ AI calls `find_grants` → Returns matching grants with deadlines

#### Citation Analysis

*"Who has cited Bengio's 2018 Turing Award paper?"*
→ AI calls `find_citations` → Returns all citing papers

#### Institution Due Diligence

*"What's the research profile of MIT's AI Lab?"*
→ AI calls `institution_research_profile` → Returns h-index, stats, top topics

***

### Pricing

| Tool | Price |
|------|-------|
| `search_papers` | $0.02/call |
| `get_paper_details` | $0.01/call |
| `find_citations` | $0.02/call |
| `find_grants` | $0.03/call |
| `institution_research_profile` | $0.05/call |
| `author_research_profile` | $0.03/call |
| `research_trends` | $0.05/call |
| `systematic_review` | $0.10/call |

No subscription required. Pay per use via Apify PPE.

***

### Tips

1. **Use specific queries** — "transformer attention NLP" returns better results than "AI"
2. **Filter by year** — Add `year_from` and `year_to` to `research_trends`
3. **Use DOI when possible** — `get_paper_details` returns more metadata with DOI
4. **Combine tools** — Call multiple tools in sequence for comprehensive research

***

### Connect to AI Agents

#### Claude Desktop

```json
{
  "mcpServers": {
    "academic-research-mcp": {
      "url": "https://academic-research-mcp.apify.actor/mcp"
    }
  }
}
```

#### Cursor / Windsurf

Add the same JSON to your AI client config.

#### cURL Example

```bash
curl -X POST "https://academic-research-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -d '{"tool": "search_papers", "params": {"query": "mRNA vaccines", "max_results": 5}}'
```

***

### Output Schema

All tools return JSON. See individual tool documentation for specific field schemas.

***

### API Status

- **Health**: Running
- **Uptime**: 99.9%
- **Rate Limits**: None enforced client-side (respect APIs' natural limits)
- **Support**: Open issue on GitHub

# Actor input Schema

## `tool` (type: `string`):

MCP tool to call

## `params` (type: `object`):

Tool parameters as JSON object

## Actor input object example

```json
{
  "tool": "search_papers",
  "params": {
    "query": "machine learning",
    "max_results": 1
  }
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "tool": "search_papers",
    "params": {
        "query": "machine learning",
        "max_results": 1
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("red.cars/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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "tool": "search_papers",
    "params": {
        "query": "machine learning",
        "max_results": 1,
    },
}

# Run the Actor and wait for it to finish
run = client.actor("red.cars/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 '{
  "tool": "search_papers",
  "params": {
    "query": "machine learning",
    "max_results": 1
  }
}' |
apify call red.cars/academic-research-mcp --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=red.cars/academic-research-mcp",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

```json
{
    "openapi": "3.0.1",
    "info": {
        "title": "Academic Research MCP — AI Agent Research Librarian",
        "description": "Search 600M+ academic papers, grants, and citations for AI agents. One tool call to CrossRef, OpenAlex, Semantic Scholar, DBLP, CORE, PubMed, NIH, and NSF.",
        "version": "1.0",
        "x-build-id": "rxCaAWCmKG6UZ1UzI"
    },
    "servers": [
        {
            "url": "https://api.apify.com/v2"
        }
    ],
    "paths": {
        "/acts/red.cars~academic-research-mcp/run-sync-get-dataset-items": {
            "post": {
                "operationId": "run-sync-get-dataset-items-red.cars-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": {
                                "$ref": "#/components/schemas/inputSchema"
                            }
                        }
                    }
                },
                "parameters": [
                    {
                        "name": "token",
                        "in": "query",
                        "required": true,
                        "schema": {
                            "type": "string"
                        },
                        "description": "Enter your Apify token here"
                    }
                ],
                "responses": {
                    "200": {
                        "description": "OK"
                    }
                }
            }
        },
        "/acts/red.cars~academic-research-mcp/runs": {
            "post": {
                "operationId": "runs-sync-red.cars-academic-research-mcp",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor and returns information about the initiated run in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$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": {
                                    "$ref": "#/components/schemas/runsResponseSchema"
                                }
                            }
                        }
                    }
                }
            }
        },
        "/acts/red.cars~academic-research-mcp/run-sync": {
            "post": {
                "operationId": "run-sync-red.cars-academic-research-mcp",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$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": {
                    "tool": {
                        "title": "Tool Name",
                        "enum": [
                            "search_papers",
                            "get_paper_details",
                            "find_citations",
                            "find_grants",
                            "institution_research_profile",
                            "author_research_profile",
                            "research_trends",
                            "systematic_review"
                        ],
                        "type": "string",
                        "description": "MCP tool to call"
                    },
                    "params": {
                        "title": "Parameters (JSON)",
                        "type": "object",
                        "description": "Tool parameters as JSON object"
                    }
                }
            },
            "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
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }
    }
}
```