# Research Paper Ingestion MCP Server
[](https://modelcontextprotocol.io)
[](https://python.org)
[](LICENSE)
[](https://github.com/marc-shade/agentic-system-oss)
> **Autonomous knowledge acquisition from academic research papers for AGI self-improvement.**
Part of the [Agentic System](https://github.com/marc-shade/agentic-system-oss) - a 24/7 autonomous AI framework with persistent memory.
## Features
### Paper Discovery
- **arXiv Integration**: Search and download from arXiv.org
- **Semantic Scholar**: Citation analysis and academic impact metrics
- **PDF Download**: Automatic paper retrieval and storage
### Knowledge Extraction
- **Insight Extraction**: Identify key findings and contributions
- **Citation Analysis**: Understand paper influence and relationships
- **Technique Identification**: Extract novel methods and approaches
### Memory Integration
- **Enhanced Memory**: Store extracted knowledge for AGI learning
- **Structured Entities**: Create searchable memory representations
- **Citation Graphs**: Track knowledge lineage
## Installation
```bash
cd ${AGENTIC_SYSTEM_PATH:-/opt/agentic}/agentic-system/mcp-servers/research-paper-mcp
pip install -r requirements.txt
```
## Configuration
Add to `~/.claude.json`:
```json
{
"mcpServers": {
"research-paper-mcp": {
"command": "python3",
"args": [
"${AGENTIC_SYSTEM_PATH:-/opt/agentic}/agentic-system/mcp-servers/research-paper-mcp/server.py"
],
"env": {},
"disabled": false
}
}
}
```
## Available Tools
### search_arxiv
Search arXiv for research papers by query.
**Parameters**:
- `query` (required): Search query (e.g., "recursive self-improvement AGI")
- `max_results`: Maximum results (default: 10)
- `sort_by`: Sort order - relevance, lastUpdatedDate, submittedDate
**Example**:
```python
results = mcp__research-paper-mcp__search_arxiv({
"query": "meta-learning neural networks",
"max_results": 20,
"sort_by": "relevance"
})
```
### search_semantic_scholar
Search Semantic Scholar for papers with citation metrics.
**Parameters**:
- `query` (required): Search query
- `fields`: Metadata fields to retrieve
- `limit`: Maximum results (default: 10)
**Example**:
```python
results = mcp__research-paper-mcp__search_semantic_scholar({
"query": "transformer architecture attention",
"fields": ["title", "authors", "citationCount", "year"],
"limit": 15
})
```
### download_paper
Download research paper PDF from URL.
**Parameters**:
- `url` (required): PDF URL
- `paper_id` (required): Unique identifier for filename
**Example**:
```python
result = mcp__research-paper-mcp__download_paper({
"url": "https://arxiv.org/pdf/1234.5678.pdf",
"paper_id": "arxiv-1234.5678"
})
```
### extract_insights
Extract key insights and findings from paper text.
**Parameters**:
- `paper_text` (required): Full paper text or abstract
- `focus_areas`: Optional specific areas to focus on
**Example**:
```python
insights = mcp__research-paper-mcp__extract_insights({
"paper_text": paper_abstract,
"focus_areas": ["methodology", "results"]
})
```
### analyze_citations
Analyze citation relationships and paper influence.
**Parameters**:
- `paper_id` (required): Semantic Scholar or arXiv paper ID
- `depth`: Citation graph depth 1-3 (default: 1)
**Example**:
```python
analysis = mcp__research-paper-mcp__analyze_citations({
"paper_id": "arxiv:1706.03762", # "Attention Is All You Need"
"depth": 2
})
```
### store_paper_knowledge
Store extracted knowledge in enhanced-memory for AGI learning.
**Parameters**:
- `paper_metadata` (required): Paper metadata dict
- `insights` (required): List of key insights
- `techniques`: List of novel techniques
**Example**:
```python
stored = mcp__research-paper-mcp__store_paper_knowledge({
"paper_metadata": {
"id": "arxiv-1234.5678",
"title": "Novel AGI Approach",
"authors": ["Smith", "Jones"],
"year": 2024
},
"insights": [
"Achieves 95% accuracy on benchmark",
"10x faster than previous methods"
],
"techniques": [
"Recursive meta-optimization",
"Self-modifying architectures"
]
})
```
## Usage Patterns
### Autonomous Research Workflow
```python
# 1. Search for relevant papers
arxiv_results = mcp__research-paper-mcp__search_arxiv({
"query": "recursive self-improvement",
"max_results": 10
})
# 2. Get citation metrics
for paper in arxiv_results['papers']:
scholar_data = mcp__research-paper-mcp__search_semantic_scholar({
"query": paper['title'],
"limit": 1
})
# 3. Download high-impact papers
if scholar_data['papers'][0]['citationCount'] > 50:
pdf = mcp__research-paper-mcp__download_paper({
"url": paper['pdf_url'],
"paper_id": paper['id']
})
# 4. Extract and store insights
insights = mcp__research-paper-mcp__extract_insights({
"paper_text": paper['abstract']
})
mcp__research-paper-mcp__store_paper_knowledge({
"paper_metadata": paper,
"insights": insights['insights']
})
```
### Citation Network Analysis
```python
# Analyze citation influence
analysis = mcp__research-paper-mcp__analyze_citations({
"paper_id": "influential-paper-id",
"depth": 2
})
# Identify most influential papers in field
if analysis['citation_graph']['influential_citations'] > 100:
# Download and study this foundational paper
pass
```
## Storage
- **Papers Directory**: `${AGENTIC_SYSTEM_PATH:-/opt/agentic}/agentic-system/research-papers/`
- **PDFs**: Saved as `{paper_id}.pdf`
- **Memory Integration**: Via enhanced-memory-mcp create_entities
## Dependencies
- **arxiv**: arXiv API Python wrapper
- **aiohttp**: Async HTTP client for Semantic Scholar API
- **mcp**: Model Context Protocol SDK
## Future Enhancements
1. **PDF Text Extraction**: Parse full paper text from PDFs
2. **Figure/Diagram Analysis**: Extract visual insights
3. **Code Repository Links**: Find implementation code
4. **Related Papers**: Automatic discovery of connected research
5. **Trend Detection**: Identify emerging research directions
6. **LLM-Powered Insight Extraction**: Use GPT-4 for deeper analysis
## Integration with AGI System
This MCP server closes Gap #1 from AGI_GAP_ANALYSIS.md:
**Knowledge Acquisition Infrastructure** ✅
- ✓ Research Paper Ingestion (arXiv + Semantic Scholar)
- ⏳ Video Transcript Processing (separate MCP)
- ⏳ GitHub Repository Analysis (future)
- ⏳ Documentation Scraping (future)
- ⏳ Knowledge Graph Integration (future)
**Impact**: System can now autonomously learn from the latest AI research!
