Merge pull request microsoft#1 from madebygps/add-part3

Add part3

Author: marlenezw

.gitignore

@@ -0,0 +1,3 @@
+.vscode/mcp.json
+AIResearchHub/__pycache__/paper_manager.cpython-313.pyc
+AIResearchHub/__pycache__/server.cpython-313.pyc

AIResearchHub/data/research_papers.json

@@ -0,0 +1,58 @@
+[
+  {
+    "id": 1,
+    "topic": "few shot learning",
+    "created": "2025-07-14T14:50:54.749520",
+    "status": "complete",
+    "papers_found": [
+      {
+        "title": "EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients",
+        "authors": "Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, St\u00e9phane Pateux, Vincent Gripon",
+        "url": "https://hf.co/papers/2201.09699"
+      },
+      {
+        "title": "Few-shot Learning with Multilingual Language Models",
+        "authors": "Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal",
+        "url": "https://hf.co/papers/2112.10668"
+      },
+      {
+        "title": "Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning",
+        "authors": "Qianyu Guo, Jingrong Wu, Tianxing Wu, Haofen Wang, Weifeng Ge, Wenqiang Zhang",
+        "url": "https://hf.co/papers/2502.01183"
+      },
+      {
+        "title": "Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference",
+        "authors": "Shell Xu Hu, Da Li, Jan St\u00fchmer, Minyoung Kim, Timothy M. Hospedales",
+        "url": "https://hf.co/papers/2204.07305"
+      }
+    ],
+    "repositories_found": [
+      {
+        "name": "oscarknagg/few-shot",
+        "url": "https://github.com/oscarknagg/few-shot",
+        "stars": 1259
+      },
+      {
+        "name": "sicara/easy-few-shot-learning",
+        "url": "https://github.com/sicara/easy-few-shot-learning",
+        "stars": 1225
+      },
+      {
+        "name": "RL-VIG/LibFewShot",
+        "url": "https://github.com/RL-VIG/LibFewShot",
+        "stars": 1028
+      },
+      {
+        "name": "huggingface/setfit",
+        "url": "https://github.com/huggingface/setfit",
+        "stars": 2522
+      },
+      {
+        "name": "tata1661/FSL-Mate",
+        "url": "https://github.com/tata1661/FSL-Mate",
+        "stars": 1746
+      }
+    ],
+    "notes": "Comprehensive research completed on few-shot learning. Found key papers covering state-of-the-art methods, environmental robustness, multilingual approaches, and simple pipeline improvements. Identified practical implementations including comprehensive libraries (LibFewShot, FSL-Mate), ready-to-use frameworks (SetFit by HuggingFace), and tutorial-focused repositories (easy-few-shot-learning). The research spans both theoretical advances and practical implementation resources."
+  }
+]

AIResearchHub/paper_manager.py

@@ -0,0 +1,96 @@
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+
+class PaperManager:
+    """Simple paper manager that matches the minimal server needs."""
+    
+    def __init__(self):
+        """Initialize with JSON storage."""
+        self.data_dir = Path("data")
+        self.data_dir.mkdir(exist_ok=True)
+        self.papers_file = self.data_dir / "research_papers.json"
+    
+    def load_papers(self) -> list[dict]:
+        """Load papers from JSON file."""
+        if self.papers_file.exists():
+            return json.loads(self.papers_file.read_text()) 
+        return []
+    
+    def save_papers(self, papers: list[dict]):
+        """Save papers to JSON file."""
+        self.papers_file.write_text(json.dumps(papers, indent=2))
+    
+    def add_research_entry(self, topic: str) -> dict[str, Any]:
+        """Add a new research entry for a topic."""
+        papers = self.load_papers()
+        
+        research_entry = {
+            "id": len(papers) + 1,
+            "topic": topic,
+            "created": datetime.now().isoformat(),
+            "status": "pending",
+            "papers_found": [],
+            "repositories_found": [],
+            "notes": ""
+        }
+        
+        papers.append(research_entry)
+        self.save_papers(papers)
+        
+        return research_entry
+    
+    def add_paper_to_research(self, research_id: int, paper_data: dict):
+        """Add a paper to an existing research entry."""
+        papers = self.load_papers()
+        
+        for entry in papers:
+            if entry["id"] == research_id:
+                entry["papers_found"].append({
+                    "title": paper_data.get("title", ""),
+                    "authors": paper_data.get("authors", ""),
+                    "url": paper_data.get("url", "")
+                })
+                break
+        
+        self.save_papers(papers)
+    
+    def add_repo_to_research(self, research_id: int, repo_data: dict):
+        """Add a repository to an existing research entry."""
+        papers = self.load_papers()
+        
+        for entry in papers:
+            if entry["id"] == research_id:
+                entry["repositories_found"].append({
+                    "name": repo_data.get("name", ""),
+                    "url": repo_data.get("url", ""),
+                    "stars": repo_data.get("stars", 0)
+                })
+                break
+        
+        self.save_papers(papers)
+    
+    def get_research_summary(self) -> dict[str, Any]:
+        """Get summary of all research activities."""
+        papers = self.load_papers()
+        
+        return {
+            "total_research": len(papers),
+            "entries": papers,
+            "last_updated": datetime.now().isoformat()
+        }
+    
+    def update_research_status(self, research_id: int, status: str, notes: str = ""):
+        """Update the status of a research entry."""
+        papers = self.load_papers()
+        
+        for entry in papers:
+            if entry["id"] == research_id:
+                entry["status"] = status
+                if notes:
+                    entry["notes"] = notes
+                break
+        
+        self.save_papers(papers)

AIResearchHub/server.py

@@ -0,0 +1,154 @@
+import json
+
+from fastmcp import FastMCP
+from paper_manager import PaperManager
+
+# Initialize MCP server and paper manager
+mcp = FastMCP("AI Research Hub")
+paper_manager = PaperManager()
+
+@mcp.tool(description="Start researching a topic and get research ID")
+async def research_topic(topic: str) -> dict:
+    """
+    Create a new research entry for tracking papers and implementations
+    
+    Args:
+        topic: Research topic to investigate
+        
+    Returns:
+        Research entry details with tracking ID
+    """
+    research_entry = paper_manager.add_research_entry(topic)
+    
+    return {
+        "success": True,
+        "topic": topic,
+        "research_id": research_entry["id"],
+        "message": f"Research entry #{research_entry['id']} created for '{topic}'",
+        "total_research_topics": len(paper_manager.load_papers())
+    }
+
+@mcp.tool(description="Get GitHub search strategies for finding implementations") 
+async def get_github_searches(topic: str) -> dict:
+    """
+    Generate GitHub search commands for finding code implementations
+    
+    Args:
+        topic: Research topic to find implementations for
+        
+    Returns:
+        GitHub search strategies and commands
+    """
+    return {
+        "success": True,
+        "topic": topic,
+        "github_searches": [f"{topic} implementation", f"{topic} python"],
+        "commands": [f"Search repos: {topic} stars:>10"],
+        "instructions": "Use GitHub MCP with these search terms"
+    }
+
+@mcp.tool(description="Add a paper to a research entry")
+async def add_paper(research_id: int, title: str, authors: str = "", url: str = "") -> dict:
+    """
+    Add a research paper to an existing research entry
+    
+    Args:
+        research_id: ID of the research entry to add to
+        title: Title of the paper
+        authors: Paper authors (optional)
+        url: URL to the paper (optional)
+        
+    Returns:
+        Success status and details
+    """
+    paper_data = {
+        "title": title,
+        "authors": authors,
+        "url": url
+    }
+    
+    paper_manager.add_paper_to_research(research_id, paper_data)
+    
+    return {
+        "success": True,
+        "research_id": research_id,
+        "paper_added": title,
+        "message": f"Paper '{title}' added to research #{research_id}"
+    }
+
+@mcp.tool(description="Add a repository to a research entry")
+async def add_repository(research_id: int, name: str, url: str = "", stars: int = 0) -> dict:
+    """
+    Add a code repository to an existing research entry
+    
+    Args:
+        research_id: ID of the research entry to add to
+        name: Repository name
+        url: Repository URL (optional)
+        stars: Star count (optional)
+        
+    Returns:
+        Success status and details
+    """
+    repo_data = {
+        "name": name,
+        "url": url,
+        "stars": stars
+    }
+    
+    paper_manager.add_repo_to_research(research_id, repo_data)
+    
+    return {
+        "success": True,
+        "research_id": research_id,
+        "repository_added": name,
+        "message": f"Repository '{name}' added to research #{research_id}"
+    }
+
+@mcp.tool(description="Update research status and add notes")
+async def update_research_status(research_id: int, status: str, notes: str = "") -> dict:
+    """
+    Update the status of a research entry
+    
+    Args:
+        research_id: ID of the research entry to update
+        status: New status (pending, active, complete)
+        notes: Optional notes about the research progress
+        
+    Returns:
+        Success status and details
+    """
+    paper_manager.update_research_status(research_id, status, notes)
+    
+    return {
+        "success": True,
+        "research_id": research_id,
+        "status": status,
+        "message": f"Research #{research_id} status updated to '{status}'"
+    }
+
+@mcp.prompt(name="research_workflow")
+def research_workflow_prompt(topic: str) -> str:
+    """Complete research workflow for any topic"""
+    return f"""Research Topic: {topic}
+
+WORKFLOW:
+1. research_topic(topic="{topic}") - Create research entry
+2. Search HuggingFace papers: mcp_huggingface_paper_search(query="{topic}")
+3. get_github_searches(topic="{topic}") - Get search strategies  
+4. Search GitHub repos: mcp_github_search_repositories(query="{topic}")
+5. Add findings: add_paper() and add_repository()
+6. update_research_status(research_id, "complete") - Mark done
+7. Check research://status for dashboard
+
+TOOLS: research_topic, add_paper, add_repository, get_github_searches, update_research_status
+GOAL: Link papers with implementations"""
+
+@mcp.resource("status://dashboard")
+def research_status() -> str:
+    """Current research status and saved topics"""
+    summary = paper_manager.get_research_summary()
+    return json.dumps(summary, indent=2)
+
+if __name__ == "__main__":
+    mcp.run()

README.md

@@ -12,6 +12,7 @@ By the end of this tutorial series, you'll have:
 ## Tutorial Structure
 
 ### [Part 1: Prerequisites and Setup](part1-setup.md)
+
 **⏱️ Time: 15-20 minutes**
 
 Set up your development environment and understand MCP fundamentals:
@@ -59,7 +60,7 @@ Challenge: Create a list comprehension that filters even numbers...
 
 ---
 
-### [Part 3: Building Your Own MCP Server - AI Research Learning Assistant](part3-ai-research-server-python.md)
+### [Part 3: Building Your Own MCP Server - AI Research Learning Hub](part3-ai-researcher.md)
 **⏱️ Time: 20-35 minutes**
 
 **Key Learning Objectives:**
@@ -83,7 +84,7 @@ Build an advanced MCP server that helps you keep up with the latest AI Research:
 - `track_learning_progress()` - Monitor study achievements and goals
 - `send_research_learning()` - Send study daily study note to the user 
 
-**Continue to**: [Part 3: AI Research Learning Hub →](./part3-ai-research.md)
+**Continue to**: [Part 3: AI Research Learning Hub →](./part3-ai-researcher.md)
 
 ---
 
@@ -94,8 +95,8 @@ If you're ready to dive in immediately:
 1. **Prerequisites**: Ensure you have VS Code, Python 3.12+, Docker and Python extension installed
 2. **Choose your path**:
    - 🆕 **Need help getting set up?** Start with [Part 1: Setup](part1-setup.md)
-   - � **Want to learn Python with MCP?** Jump to [Part 2: Python Study Buddy](part2-study-buddy.md)
-   - 🧠 **Ready to build AI research tools?** Go to [Part 3: AI Research Server](part3-ai-research-server.md)
+   - 🐍 **Want to learn Python with MCP?** Jump to [Part 2: Python Study Buddy](part2-study-buddy.md)
+   - 🧠 **Ready to build AI research tools?** Go to [Part 3: AI Research Hub](part3-ai-researcher.md)
 
 ## Repository Structure
 
@@ -104,7 +105,7 @@ letslearnmcp-python/
 ├── README.md                        # This overview
 ├── part1-setup.md                   # Prerequisites and environment setup
 ├── part2-study-buddy.md             # Building Python learning companion
-└── part3-ai-research-server.md      # Creating AI research discovery server
+└── part3-ai-researcher.md           # Creating AI research discovery server
 ```
 
 ## Additional Resources