AMiner MCP Server
Access AMiner's free academic data APIs to search researchers, papers, and patents, enabling AI agents to retrieve and utilize global research information.
README
AMiner MCP Server
💡 Core Advantage: Free Academic Data Access
Currently, AMiner's core search interfaces (Scholar, Paper, and Patent Search) are free to use. This allows you to integrate massive global research data into your AI workflow with zero API costs, making it the most cost-effective solution for academic automation.
This directory provides a Model Context Protocol (MCP) server for accessing AMiner's open platform API.
Quick Install
Installation
pip install -e .
Configuration
Set your AMiner API token as an environment variable:
Windows (PowerShell):
$env:AMINER_TOKEN="your_token_here"
Linux/Mac:
export AMINER_TOKEN="your_token_here"
Or create a .env file in your project root:
AMINER_TOKEN=your_token_here
Usage
Run the server:
python -m aminer_mcp
Or use the command-line shortcut:
aminer-mcp
Features
- Scholar Search: Find researchers by name or organization.
- Paper Search: Find papers by title.
- Patent Search: Search patents by keywords.
Setup
-
Install Dependencies:
pip install "mcp[cli]" requests -
Get API Token:
- Register at AMiner Open Platform.
- Generate your API Key/Token.
-
Configure Environment: You can either set the
AMINER_TOKENenvironment variable manually or use the provided.envfile.Option A: Using .env (Recommended) A
.envfile has been created for you with your token. The server will automatically load it.Option B: Manual Environment Variable
Windows (PowerShell):
$env:AMINER_TOKEN="your_token_here"Linux/Mac:
export AMINER_TOKEN="your_token_here"
Running the Server
You can run the server directly or use it with an MCP client (like Claude Desktop or generic IDEs).
Quick Start (Recommended)
For a guided startup experience with environment and dependency checks:
Windows:
.\start_server.ps1
Linux/Mac:
chmod +x start_server.sh
./start_server.sh
Direct Run (stdio)
python server.py
Inspecting with MCP CLI
You can use the MCP CLI to inspect and test the server.
mcp dev server.py
Tools Available
-
search_scholar:
name: Scholar name.org: Organization.offset: Pagination offset.size: Result count.
-
search_paper:
title: Paper title.page: Page number.size: Result count.
-
search_patent:
query: Search query.page: Page number.size: Result count.
-
get_papers_by_ids:
ids: Comma-separated list of paper IDs.
-
search_paper_pro (0.01 CNY/call):
- Fallback Tool: Use only when strictly necessary.
title,keyword,author,page,size.
API References
Configuration for Cursor IDE
To use this MCP server in Cursor editor, add the following configuration:
Windows (Cursor config path: %APPDATA%\Cursor\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json):
{
"mcpServers": {
"aminer": {
"command": "python",
"args": ["-m", "aminer_mcp"],
"env": {
"AMINER_TOKEN": "<YOUR_AMINER_TOKEN>"
}
}
}
}
Linux/Mac:
{
"mcpServers": {
"aminer": {
"command": "python3",
"args": ["-m", "aminer_mcp"],
"env": {
"AMINER_TOKEN": "<YOUR_AMINER_TOKEN>"
}
}
}
}
Note: Replace <YOUR_AMINER_TOKEN> with your actual AMiner API token from AMiner Open Platform.
Contributing
Issues and Pull Requests are welcome!
Usage as an Agent Skill
This repository also includes an Agent Skill definition, allowing AI agents (like Cursor's Agent) to use AMiner search capabilities directly without a full MCP server setup for local tasks.
Skill Location: .agent/skills/aminer-search/
How it works
The skill wraps the AMiner client code into a simple CLI tool that agents can invoke to perform searches.
Prerequisite
Ensure the AMINER_TOKEN environment variable is set in your terminal or .env file.
Skill Capabilities
- Paper Search:
python .agent/skills/aminer-search/scripts/search_tool.py paper --title "..." - Scholar Search:
python .agent/skills/aminer-search/scripts/search_tool.py scholar --name "..." - Patent Search:
python .agent/skills/aminer-search/scripts/search_tool.py patent --query "..."
Agents detecting this skill will automatically know how to use these commands to fetch academic data for you.
License
MIT License
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.