Google Scholar Search Tool
An academic literature search tool based on Google Scholar API, providing professional search capabilities for academic papers, research reports, and technical literature.
When to Activate
Automatically activate when the user mentions the following:
Academic Search Keywords
- "papers", "academic", "literature", "research"
- "search papers", "find literature", "academic research"
- "Google Scholar", "Scholar"
Specific Scenarios
- Need to find academic papers or research reports
- Need to understand academic progress in a certain field
- Need to find works by specific authors
- Need to obtain citation information and publication journals
- Need to research theoretical basis in technical fields
Example Questions
- "Help me search for papers on machine learning"
- "Find applications of deep learning in NLP"
- "Research academic papers on Transformer architecture"
- "Find some literature on large model training methods"
- "Search for papers related to Attention mechanism"
Tools
serper_scholar
Purpose: Perform academic literature search and return detailed paper information
Parameters:
- (required, string): Search keywords
- (optional, number): Number of returned results, default 10, maximum 20
- (optional, string): Country code, default cn
- Recommended values: cn (China), us (United States), uk (United Kingdom)
- (optional, string): Language code, default zh-CN
- Recommended values: zh-CN (Simplified Chinese), en (English)
Return Fields:
- : Paper title
- : Paper link
- : Abstract
- : Literature type (PDF, HTML, etc.)
- : Publication year
- : Author list
- : Publication journal/conference
- : Citation count
Best Practices
1. Search Techniques
Use professional terms and technical keywords:
Examples:
- ✅ "Attention mechanism neural machine translation"
- ✅ "Transformer large language models"
- ✅ "Reinforcement learning robotics"
- ❌ "Machine learning" (Too broad, too many results)
2. Add Field Limitations
Clearly define research fields and methods:
Examples:
- ✅ "BERT semantic analysis NLP"
- ✅ "CNN image classification computer vision"
- ✅ "GPT text generation natural language"
- ✅ "Q-learning reinforcement learning agent"
3. Time Range Search
Focus on the latest research progress:
Examples:
- ✅ "Large language models 2024 2025"
- ✅ "Transformer architecture recent advances"
- ✅ "Diffusion models 2023 2024"
4. Author and Institution Search
Find works by specific researchers or institutions:
Examples:
- ✅ "Geoffrey Hinton deep learning"
- ✅ "Yann LeCun CNN papers"
- ✅ "Andrew Ng machine learning"
- ✅ "OpenAI research papers"
5. Paper Type Filtering
Focus on specific types of literature:
Examples:
- ✅ "Survey deep learning"
- ✅ "Review transformer models"
- ✅ "Tutorial reinforcement learning"
- ✅ "Benchmark NLP models"
6. Result Quantity Selection
Adjust according to needs:
- Quick browsing: (core literature)
- Comprehensive understanding: (mainstream research)
- In-depth research: (full coverage)
7. Citation Information Analysis
Focus on highly cited papers and classic literature:
Key Points:
- Citation count: Papers with high are usually field classics
- Publication year: Newer papers represent the latest progress
- Publication journal: Top conferences (NeurIPS, ICML, ACL) have high quality
Example Scenarios
Scenario 1: Technical Research
User Query: "Research papers on Transformer architecture"
AI Action:
javascript
// 搜索核心论文
serper_scholar({
query: "Transformer architecture attention is all you need",
num: 5
})
// 搜索后续研究
serper_scholar({
query: "BERT transformer NLP 2018 2019",
num: 8
})
Response Example:
Core Papers:
1. **Attention Is All You Need** (2017)
- Authors: Vaswani et al.
- Citations: 100,000+
- 📄 [PDF]
Follow-up Research:
2. **BERT: Pre-training of Deep Bidirectional Transformers** (2019)
- Authors: Devlin et al.
- Citations: 80,000+
- Published: NAACL 2019
3. **Improving Language Understanding with Generative Pre-Training** (2018)
- Authors: Radford et al. (OpenAI)
- Citations: 50,000+
Scenario 2: Field Review
User Query: "Find some review papers on reinforcement learning"
AI Action:
javascript
serper_scholar({
query: "Survey review reinforcement learning",
num: 8
})
Response Example:
Reinforcement Learning Review Papers:
1. **Reinforcement Learning: A Survey** (2021)
- Citations: 5,000+
- Type: Review
2. **Deep Reinforcement Learning: An Overview** (2020)
- Citations: 8,000+
- Type: Review
3. **Recent Advances in Deep Reinforcement Learning** (2022)
- Citations: 2,000+
- Type: Review
Scenario 3: Author Research
User Query: "Find Geoffrey Hinton's deep learning papers"
AI Action:
javascript
serper_scholar({
query: "Geoffrey Hinton deep learning backpropagation",
num: 10
})
Response Example:
Geoffrey Hinton's Classic Papers:
1. **Deep Learning** (2015)
- Co-authors: LeCun, Bengio
- Published: Nature
- Citations: 50,000+
2. **Backpropagation Applied to Handwritten Zip Code Recognition** (1989)
- Citations: 10,000+
3. **Reducing the Dimensionality of Data with Neural Networks** (2006)
- Citations: 30,000+
Scenario 4: Latest Progress
User Query: "What are the new advances in large models in 2024-2025"
AI Action:
javascript
serper_scholar({
query: "Large language models 2024 2025 advances",
num: 10
})
Response Example:
2024-2025 Large Model Research Advances:
1. **Scaling Laws for Neural Language Models** (2024)
- Continued exploration of the relationship between model scale and performance
2. **Instruction Tuning for Large Language Models** (2024)
- Improvements in instruction tuning methods
3. **Chain-of-Thought Prompting** (2024)
- Chain-of-thought prompting technology
Scenario 5: Interdisciplinary Search
User Query: "Search for papers on the application of machine learning in medical diagnosis"
AI Action:
javascript
serper_scholar({
query: "Machine learning medical diagnosis healthcare",
num: 8
})
Response Example:
Machine Learning in Medical Diagnosis:
1. **Deep Learning for Medical Image Analysis** (2021)
- Citations: 8,000+
- Application: Image diagnosis
2. **Machine Learning in Clinical Diagnosis** (2022)
- Citations: 3,000+
- Application: Auxiliary diagnosis
3. **AI in Healthcare: A Survey** (2023)
- Citations: 2,000+
- Type: Review
Limitations
- Search Result Source: Google Scholar, may be affected by region
- Access Restrictions: Some papers require subscription or paid access
- Result Quantity: Maximum 20 results
- Update Delay: Latest papers may take time to be indexed
- Language Preference: Number of English papers is much higher than Chinese
Configuration
Environment Variable Configuration
bash
SERPER_API_KEY=your-api-key-here
Get API Key
Visit
https://serper.dev/ to register and obtain API Key.
Free quota: 2,500 calls per month (shared between Web and Scholar).
Related Tools
- serper_search: General web search
- web_fetch: Get detailed content of a single webpage
Tips
- Hybrid Use: First use serper_search to understand concepts, then use serper_scholar for in-depth research
- Citation Priority: Prioritize reading highly cited papers (usually field classics)
- Focus on Year: Balance classic literature and latest research
- Track Authors: After finding important authors, search all their works
- PDF Access: Try accessing the paper page to find free versions
Version History
- v1.0 (2026-02-06): Initial version with basic academic search functionality
- Supports Google Scholar API
- Provides detailed paper information (authors, year, citations, etc.)
- Integrated with OpenClaw Skill system
💡 Tip: When conducting academic searches, try to use English keywords as the quantity and quality of English papers are usually higher.