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<img src="img/oam_logo_rectangular.png" alt="OpenAlex MCP Server" width="600"/>
# OpenAlex Author Disambiguation MCP Server
[](https://modelcontextprotocol.io/)
[](https://python.org)
[](https://openalex.org)
[](LICENSE)
[](https://github.com/drAbreu/alex-mcp)
</div>
A **streamlined** Model Context Protocol (MCP) server for author disambiguation and academic research using the OpenAlex.org API. Specifically designed for AI agents with optimized data structures and enhanced functionality.
---
## 🎯 Key Features
### 🔍 **Core Capabilities**
- **Advanced Author Disambiguation**: Handles complex career transitions and name variations
- **Institution Resolution**: Current and past affiliations with transition tracking
- **Academic Work Retrieval**: Journal articles, letters, and research papers
- **Citation Analysis**: H-index, citation counts, and impact metrics
- **ORCID Integration**: Highest accuracy matching with ORCID identifiers
### 🚀 **AI Agent Optimized**
- **Streamlined Data**: Focused on essential information for disambiguation
- **Fast Processing**: Optimized data structures for rapid analysis
- **Smart Filtering**: Enhanced filtering options for targeted queries
- **Clean Output**: Structured responses optimized for AI reasoning
### 🤖 **Agent Integration**
- **Multiple Candidates**: Ranked results for automated decision-making
- **Structured Responses**: Clean, parseable output optimized for LLMs
- **Error Handling**: Graceful degradation with informative messages
- **Enhanced Filtering**: Journal-only, citation thresholds, and temporal filters
### 🏛️ **Professional Grade**
- **MCP Best Practices**: Built with FastMCP following official guidelines
- **Tool Annotations**: Proper MCP tool annotations for optimal client integration
- **Resource Management**: Efficient HTTP client management and cleanup
- **Rate Limiting**: Respectful API usage with proper delays
---
## 🚀 Quick Start
### Prerequisites
- Python 3.10 or higher
- MCP-compatible client (e.g., Claude Desktop)
- Email address (for OpenAlex API courtesy)
### Installation
For detailed installation instructions, see [INSTALL.md](INSTALL.md).
1. **Clone the repository:**
```bash
git clone https://github.com/drAbreu/alex-mcp.git
cd alex-mcp
```
2. **Create a virtual environment:**
```bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install the package:**
```bash
pip install -e .
```
4. **Configure environment:**
```bash
export OPENALEX_MAILTO=your-email@domain.com
```
5. **Run the server:**
```bash
./run_alex_mcp.sh
# Or, if installed as a CLI tool:
alex-mcp
```
---
## ⚙️ MCP Configuration
### Claude Desktop Configuration
Add to your Claude Desktop configuration file:
```json
{
"mcpServers": {
"alex-mcp": {
"command": "/path/to/alex-mcp/run_alex_mcp.sh",
"env": {
"OPENALEX_MAILTO": "your-email@domain.com"
}
}
}
}
```
Replace `/path/to/alex-mcp` with the actual path to the repository on your system.
---
## 🤖 Using with AI Agents
### OpenAI Agents Integration
You can load this MCP server in your OpenAI agent workflow using the [`agents.mcp.MCPServerStdio`](https://github.com/openai/openai-agents) interface:
```python
from agents.mcp import MCPServerStdio
async with MCPServerStdio(
name="OpenAlex MCP For Author disambiguation and works",
cache_tools_list=True,
params={
"command": "uvx",
"args": [
"--from", "git+https://github.com/drAbreu/alex-mcp.git@4.1.0",
"alex-mcp"
],
"env": {
"OPENALEX_MAILTO": "your-email@domain.com"
}
},
client_session_timeout_seconds=10
) as alex_mcp:
await alex_mcp.connect()
tools = await alex_mcp.list_tools()
print(f"Available tools: {[tool.name for tool in tools]}")
```
### Academic Research Agent Integration
This MCP server is specifically optimized for academic research workflows:
```python
# Optimized for academic research workflows
from alex_agent import run_author_research
# Enhanced functionality with streamlined data
result = await run_author_research(
"Find J. Abreu at EMBO with recent publications"
)
# Clean, structured output for AI processing
print(f"Success: {result['workflow_metadata']['success']}")
print(f"Quality: {result['research_result']['metadata']['result_analysis']['quality_score']}/100")
```
### Direct Launch with uvx
```bash
# Standard launch
uvx --from git+https://github.com/drAbreu/alex-mcp.git@4.1.0 alex-mcp
# With environment variables
OPENALEX_MAILTO=your-email@domain.com uvx --from git+https://github.com/drAbreu/alex-mcp.git@4.1.0 alex-mcp
```
---
## 🛠️ Available Tools
### 1. **autocomplete_authors** ⭐ NEW
Get multiple author candidates using OpenAlex autocomplete API for intelligent disambiguation.
**Parameters:**
- `name` (required): Author name to search (e.g., "James Briscoe", "M. Ralser")
- `context` (optional): Context for disambiguation (e.g., "Francis Crick Institute developmental biology")
- `limit` (optional): Maximum candidates (1-10, default: 5)
**Key Features:**
- ⚡ **Fast**: ~200ms response time
- 🎯 **Smart**: Multiple candidates with institutional hints
- 🧠 **AI-Ready**: Perfect for context-based selection
- 📊 **Rich**: Works count, citations, institution info
**Streamlined Output:**
```json
{
"query": "James Briscoe",
"context": "Francis Crick Institute",
"total_candidates": 3,
"candidates": [
{
"openalex_id": "https://openalex.org/A5019391436",
"display_name": "James Briscoe",
"institution_hint": "The Francis Crick Institute, UK",
"works_count": 415,
"cited_by_count": 24623,
"external_id": "https://orcid.org/0000-0002-1020-5240"
}
]
}
```
**Usage Pattern:**
```python
# Get multiple candidates for disambiguation
candidates = await autocomplete_authors(
"James Briscoe",
context="Francis Crick Institute developmental biology"
)
# AI selects best match based on institutional context
# Much more accurate than single search result!
```
### 2. **search_authors**
Search for authors with streamlined output for AI agents.
**Parameters:**
- `name` (required): Author name to search
- `institution` (optional): Institution name filter
- `topic` (optional): Research topic filter
- `country_code` (optional): Country code filter (e.g., "US", "DE")
- `limit` (optional): Maximum results (1-25, default: 20)
**Streamlined Output:**
```json
{
"query": "J. Abreu",
"total_count": 3,
"results": [
{
"id": "https://openalex.org/A123456789",
"display_name": "Jorge Abreu-Vicente",
"orcid": "https://orcid.org/0000-0000-0000-0000",
"display_name_alternatives": ["J. Abreu-Vicente", "Jorge Abreu Vicente"],
"affiliations": [
{
"institution": {
"display_name": "European Molecular Biology Organization",
"country_code": "DE"
},
"years": [2023, 2024, 2025]
}
],
"cited_by_count": 316,
"works_count": 25,
"summary_stats": {
"h_index": 9,
"i10_index": 5
},
"x_concepts": [
{
"display_name": "Astrophysics",
"score": 0.8
},
{
"display_name": "Machine Learning",
"score": 0.6
}
]
}
]
}
```
**Features**: Clean structure optimized for AI reasoning and disambiguation
---
### 2. **retrieve_author_works**
Retrieve works for a given author with enhanced filtering capabilities.
**Parameters:**
- `author_id` (required): OpenAlex author ID
- `limit` (optional): Maximum results (1-50, default: 20)
- `order_by` (optional): "date" or "citations" (default: "date")
- `publication_year` (optional): Filter by specific year
- `type` (optional): Work type filter (e.g., "journal-article")
- `authorships_institutions_id` (optional): Filter by institution
- `is_retracted` (optional): Filter retracted works
- `open_access_is_oa` (optional): Filter by open access status
**Enhanced Output:**
```json
{
"author_id": "https://openalex.org/A123456789",
"total_count": 25,
"results": [
{
"id": "https://openalex.org/W123456789",
"title": "A platform for the biomedical application of large language models",
"doi": "10.1038/s41587-024-02534-3",
"publication_year": 2025,
"type": "journal-article",
"cited_by_count": 42,
"authorships": [
{
"author": {
"display_name": "Jorge Abreu-Vicente"
},
"institutions": [
{
"display_name": "European Molecular Biology Organization"
}
]
}
],
"locations": [
{
"source": {
"display_name": "Nature Biotechnology",
"type": "journal"
}
}
],
"open_access": {
"is_oa": true
},
"primary_topic": {
"display_name": "Biomedical Engineering"
}
}
]
}
```
**Features**: Comprehensive work data with flexible filtering for targeted queries
---
## 📊 Data Optimization
### Focused Information Architecture
This MCP server provides focused, structured data specifically designed for AI agent consumption:
### Author Data Features
- **Identity Resolution**: Names, ORCID, alternatives for disambiguation
- **Affiliation Tracking**: Current and historical institutional connections
- **Impact Metrics**: Citation counts, h-index, and scholarly impact
- **Research Context**: Fields, concepts, and domain expertise
- **Career Analysis**: Temporal affiliation changes and transitions
### Work Data Features
- **Publication Metadata**: Title, DOI, venue, and publication details
- **Impact Assessment**: Citation counts and scholarly influence
- **Access Information**: Open access status and availability
- **Authorship Details**: Complete author lists and institutional affiliations
- **Research Classification**: Topics, concepts, and domain categorization
### Enhanced Filtering
```python
# Target high-impact journal articles
works = await retrieve_author_works(
author_id="https://openalex.org/A123456789",
type="journal-article", # Focus on journal publications
open_access_is_oa=True, # Open access only
order_by="citations", # Most cited first
limit=15
)
# Career transition analysis
authors = await search_authors(
name="J. Abreu",
institution="EMBO", # Current institution
topic="Machine Learning", # Research focus
limit=10
)
```
---
## 🧪 Example Usage
### Author Disambiguation
```python
from alex_mcp.server import search_authors_core
# Comprehensive author search
results = search_authors_core(
name="J Abreu Vicente",
institution="EMBO",
topic="Machine Learning",
limit=20
)
print(f"Found {results.total_count} candidates")
for author in results.results:
print(f"- {author.display_name}")
if author.affiliations:
current_inst = author.affiliations[0].institution.display_name
print(f" Institution: {current_inst}")
print(f" Metrics: {author.cited_by_count} citations, h-index {author.summary_stats.h_index}")
if author.x_concepts:
fields = [c.display_name for c in author.x_concepts[:3]]
print(f" Research: {', '.join(fields)}")
```
### Academic Work Analysis
```python
from alex_mcp.server import retrieve_author_works_core
# Comprehensive work retrieval
works = retrieve_author_works_core(
author_id="https://openalex.org/A5058921480",
type="journal-article", # Academic focus
order_by="citations", # Impact-based ordering
limit=20
)
print(f"Found {works.total_count} publications")
for work in works.results:
print(f"- {work.title}")
if work.locations:
journal = work.locations[0].source.display_name
print(f" Published in: {journal} ({work.publication_year})")
print(f" Impact: {work.cited_by_count} citations")
if work.open_access and work.open_access.is_oa:
print(" ✓ Open Access")
```
### Institution and Field Analysis
```python
# Analyze career transitions
def analyze_career_path(author_result):
affiliations = author_result.affiliations
if len(affiliations) > 1:
print("Career path:")
for aff in sorted(affiliations, key=lambda x: min(x.years)):
years = f"{min(aff.years)}-{max(aff.years)}"
print(f" {years}: {aff.institution.display_name}")
# Research evolution
if author_result.x_concepts:
print("Research areas:")
for concept in author_result.x_concepts[:5]:
print(f" {concept.display_name} (score: {concept.score:.2f})")
# Usage
results = search_authors_core("Jorge Abreu Vicente")
if results.results:
analyze_career_path(results.results[0])
```
---
## 🔧 Configuration Options
### Environment Variables
```bash
# Required
export OPENALEX_MAILTO=your-email@domain.com
# Optional settings
export OPENALEX_MAX_AUTHORS=100 # Maximum authors per query
export OPENALEX_USER_AGENT=research-agent-v1.0
export ALEX_MCP_VERSION=4.1.0
# Rate limiting (respectful usage)
export OPENALEX_RATE_PER_SEC=10
export OPENALEX_RATE_PER_DAY=100000
```
### Performance Tuning
```python
# For comprehensive research applications
config = {
"max_authors_per_query": 25, # Detailed author analysis
"max_works_per_author": 50, # Complete publication history
"enable_all_filters": True, # Full filtering capabilities
"detailed_affiliations": True, # Complete institutional data
"research_concepts": True # Detailed concept analysis
}
```
---
## 🧑💻 Development & Testing
### Project Structure
```
alex-mcp/
├── src/alex_mcp/
│ ├── server.py # Main MCP server
│ ├── data_objects.py # Data models and structures
│ └── utils.py # Utility functions
├── examples/
│ ├── basic_usage.py # Simple examples
│ ├── advanced_queries.py # Complex query examples
│ └── integration_demo.py # AI agent integration
├── tests/
│ ├── test_server.py # Server functionality tests
│ └── test_integration.py # Integration tests
└── docs/
└── api_reference.md # Detailed API documentation
```
### Running Tests
```bash
# Install test dependencies
pip install -e ".[test]"
# Run functionality tests
pytest tests/test_server.py -v
# Test with real queries
python examples/basic_usage.py
# Test AI agent integration
python examples/integration_demo.py
```
### Development Examples
```bash
# Test author disambiguation
python examples/basic_usage.py --query "J. Abreu" --institution "EMBO"
# Test work retrieval
python examples/advanced_queries.py --author-id "A123456789" --type "journal-article"
# Test integration patterns
python examples/integration_demo.py --workflow "career-analysis"
```
---
## 📈 Integration Examples
### Academic Research Workflows
Perfect integration with AI-powered research analysis:
```python
# Enhanced academic research agent
from alex_agent import AcademicResearchAgent
agent = AcademicResearchAgent(
mcp_servers=[alex_mcp], # Streamlined data processing
model="gpt-4.1-2025-04-14"
)
# Complex research queries with structured data
result = await agent.research_author(
"Find J. Abreu at EMBO with machine learning publications"
)
# Rich, structured output for AI reasoning
print(f"Quality Score: {result.quality_score}/100")
print(f"Author disambiguation: {result.confidence}")
print(f"Research fields: {result.research_domains}")
```
### Multi-Agent Systems
```python
# Collaborative research analysis
async def research_collaboration_network(seed_author):
# Find primary author
authors = await alex_mcp.search_authors(seed_author)
primary = authors['results'][0]
# Get their works
works = await alex_mcp.retrieve_author_works(
primary['id'],
type="journal-article"
)
# Analyze co-authors and build network
collaborators = set()
for work in works['results']:
for authorship in work.get('authorships', []):
collaborators.add(authorship['author']['display_name'])
return {
'primary_author': primary,
'publication_count': len(works['results']),
'collaborator_network': list(collaborators),
'research_impact': sum(w['cited_by_count'] for w in works['results'])
}
```
---
## 🤝 Contributing
We welcome contributions to improve functionality and add new features:
1. **Fork the repository**
2. **Create a feature branch**: `git checkout -b feature/enhanced-filtering`
3. **Add tests**: Ensure your changes maintain data quality and structure
4. **Submit a pull request**: Include examples and documentation
### Development Priorities
- [ ] Enhanced filtering capabilities
- [ ] Additional data enrichment
- [ ] Performance optimizations
- [ ] Integration examples
- [ ] Documentation improvements
---
## 📄 License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.
---
## 🌐 Links
- [OpenAlex API Documentation](https://docs.openalex.org/)
- [Model Context Protocol](https://modelcontextprotocol.io/)
- [FastMCP](https://github.com/ContextualAI/fastmcp)
- [OpenAI Agents](https://github.com/openai/openai-agents)
- [Academic Research Examples](examples/)
