Add comprehensive learning guide for Science Storyteller project

LEARNING_GUIDE.md

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+# Science Storyteller - Learning Guide
+
+> **For developers new to async Python, OOP, and MCP protocol**  
+> A step-by-step guide to understanding the Science Storyteller codebase
+
+---
+
+## 📚 Table of Contents
+
+0. [Architecture](#architecture)
+1. [Learning Philosophy](#learning-philosophy)
+2. [Object-Oriented Programming Basics](#object-oriented-programming-basics)
+3. [Async/Await Deep Dive](#asyncawait-deep-dive)
+4. [Module-by-Module Learning Path](#module-by-module-learning-path)
+5. [Hands-On Exercises](#hands-on-exercises)
+6. [Common Patterns Explained](#common-patterns-explained)
+7. [Debugging Tips](#debugging-tips)
+8. [Further Resources](#further-resources)
+9. [Testing Strategy](#-testing-strategy)
+
+---
+
+## Architecture 
+
+This diagram shows how a user request flows through the system.
+
+```mermaid
+graph TD
+    subgraph User Interface
+        A[Gradio UI]
+    end
+
+    subgraph Orchestration Layer
+        B(app.py: ScienceStoryteller)
+    end
+
+    subgraph Agent Layer
+        C[agents/research_agent.py]
+        D[agents/analysis_agent.py]
+        E[agents/audio_agent.py]
+    end
+
+    subgraph Tool Layer
+        F(mcp_tools/arxiv_tool.py)
+        G(mcp_tools/llm_tool.py)
+        H(ElevenLabs API)
+    end
+
+    subgraph External Services
+        I[arXiv MCP Server]
+        J[Anthropic Claude API]
+        K[ElevenLabs TTS Service]
+    end
+
+    A -- User Input (Topic) --> B
+    B -- 1. search(topic) --> C
+    C -- 2. search_papers(query) --> F
+    F -- 3. call_tool --> I
+    I -- 4. Paper Results --> F
+    F -- 5. Papers --> C
+    C -- 6. Papers --> B
+    B -- 7. summarize_and_script(paper) --> D
+    D -- 8. summarize_paper(paper) --> G
+    G -- 9. API Call --> J
+    J -- 10. Summary --> G
+    G -- 11. Summary --> D
+    D -- 12. Script --> B
+    B -- 13. text_to_speech(script) --> E
+    E -- 14. API Call --> H
+    H -- 15. API Call --> K
+    K -- 16. Audio MP3 --> H
+    H -- 17. Audio File Path --> E
+    E -- 18. Audio Path --> B
+    B -- 19. Results (Summary, Audio, etc.) --> A
+```
+
+---
+
+## Python Function Basics
+
+Functions are the primary way to group code into reusable blocks. Let's break down a function from our codebase: `utils/audio_processor.py`.
+
+```python
+def process_audio_file(audio_path: str) -> Optional[str]:
+    """
+    Process and validate an audio file.
+    
+    Args:
+        audio_path: Path to audio file
+        
+    Returns:
+        Validated path or None if invalid
+    """
+    # ... function body ...
+    return str(path)
+```
+
+### Anatomy of a Function
+
+Let's look at each part of the function definition:
+
+1.  **`def` keyword**: This signals the start of a function definition.
+2.  **Function Name**: `process_audio_file`. This is how you'll call the function later. It should be descriptive and follow the `snake_case` convention (all lowercase with underscores).
+3.  **Parameters (in `()`)**: `(audio_path: str)`. These are the inputs the function accepts.
+    -   `audio_path`: The name of the parameter.
+    -   `: str`: This is a **type hint**. It tells developers that this function expects `audio_path` to be a string. It helps with code readability and catching errors.
+4.  **Return Type Hint**: `-> Optional[str]`. This indicates what the function will return.
+    -   `Optional[str]` means the function can return either a `str` (string) or `None`. This is very useful for functions that might not always have a valid result to give back.
+5.  **Docstring**: The triple-quoted string `"""..."""` right after the definition. It explains the function's purpose, arguments (`Args`), and return value (`Returns`). This is essential for documentation.
+6.  **Function Body**: The indented code block below the definition. This is where the function's logic is implemented.
+7.  **`return` statement**: This keyword exits the function and passes back a value to whoever called it.
+
+### Why Use Functions?
+
+-   **Reusability**: Write code once and use it many times.
+-   **Modularity**: Break down complex problems into smaller, manageable pieces.
+-   **Readability**: Well-named functions make code easier to understand.
+
+---
+
+## Learning Philosophy
+
+### Why Learn Module-by-Module?
+
+**Bottom-up approach** is recommended for this project:
+1. Start with simple utilities (pure Python functions)
+2. Progress to MCP tools (understand protocol basics)
+3. Study agents (business logic and coordination)
+4. Finally tackle orchestration (integration)
+
+**Benefits:**
+- ✅ Build confidence with simple concepts first
+- ✅ Understand dependencies before integration
+- ✅ Easier to debug when you know each piece
+- ✅ Can test components independently
+
+### Learning vs Building Trade-off
+
+For a hackathon project, you need to balance:
+- **Deep understanding**: Takes time, prevents bugs
+- **Quick delivery**: Ship working product by deadline
+
+**Recommended approach for this project:**
+- **Week 1**: Deep dive into 2-3 core modules
+- **Week 2**: Implement and integrate
+- **Week 3**: Test, polish, document
+
+---
+
+## Object-Oriented Programming Basics
+
+### What is a Class?
+
+A **class** is a blueprint for creating objects. Think of it as a cookie cutter.
+
+```python
+class ScienceStoryteller:  # The blueprint
+    """Main orchestrator for the Science Storyteller workflow."""
+```
+
+### Creating Objects (Instantiation)
+
+```python
+# Creating an object from the class
+storyteller = ScienceStoryteller()  # Now you have a specific storyteller object
+```
+
+### The `__init__` Method (Constructor)
+
+The `__init__` method is called **automatically** when you create a new object.
+
+```python
+class ScienceStoryteller:
+    def __init__(self):  # Runs when ScienceStoryteller() is called
+        self.research_agent = ResearchAgent()
+        self.analysis_agent = AnalysisAgent()
+        self.audio_agent = AudioAgent()
+```
+
+**Purpose:** Set up the initial state of your object.
+
+**When it runs:**
+```python
+storyteller = ScienceStoryteller()  # __init__ runs here automatically
+```
+
+### Understanding `self`
+
+`self` refers to **this particular object instance**.
+
+```python
+class ScienceStoryteller:
+    def __init__(self):
+        self.research_agent = ResearchAgent()  # Attach to THIS object
+    
+    async def process_topic(self, topic: str):
+        papers = await self.research_agent.search(topic)  # Use THIS object's agent
+```
+
+**Why `self`?** So each object can have its own separate data.
+
+```python
+storyteller1 = ScienceStoryteller()  # Has its own research_agent
+storyteller2 = ScienceStoryteller()  # Has a different research_agent
+```
+
+### Attributes (Instance Variables)
+
+**Attributes** store data that belongs to an object.
+
+```python
+self.research_agent = ResearchAgent()  # This is an attribute
+self.analysis_agent = AnalysisAgent()  # This is an attribute
+```
+
+**Accessing attributes:**
+```python
+async def process_topic(self, topic: str):
+    # Use the attributes we created in __init__
+    papers = await self.research_agent.search(topic)
+    best_paper = await self.analysis_agent.select_best(papers, topic)
+```
+
+### Methods (Functions in a Class)
+
+**Methods** define what an object can **do**.
+
+```python
+class ScienceStoryteller:
+    async def process_topic(self, topic: str):  # This is a method
+        """Process a research topic into a podcast."""
+        # ... implementation ...
+    
+    def _format_paper_info(self, paper: dict) -> str:  # Another method
+        """Format paper metadata for display."""
+        # ... implementation ...
+```
+
+**Key points:**
+- First parameter is always `self`
+- Called using dot notation: `storyteller.process_topic("AI")`
+- Can access attributes: `self.research_agent`
+
+### Public vs Private Naming Convention
+
+```python
+def process_topic(self, topic):     # Public - no underscore
+    """Meant to be called from outside the class."""
+    
+def _format_paper_info(self, paper): # Private - starts with _
+    """Internal helper, not meant to be called externally."""
+```
+
+**Convention (not enforced):**
+- `method_name` → Public, part of the API
+- `_method_name` → Private, internal use only
+
+### Complete Example
+
+```python
+class ScienceStoryteller:
+    """Main orchestrator for the Science Storyteller workflow."""
+    
+    # Constructor - runs when object is created
+    def __init__(self):
+        self.research_agent = ResearchAgent()      # Attribute
+        self.analysis_agent = AnalysisAgent()      # Attribute
+        self.audio_agent = AudioAgent()            # Attribute
+    
+    # Public method - main workflow
+    async def process_topic(self, topic: str):
+        papers = await self.research_agent.search(topic)  # Use attribute
+        best_paper = await self.analysis_agent.select_best(papers)
+        paper_info = self._format_paper_info(best_paper)  # Call private method
+        return paper_info
+    
+    # Private method - internal helper
+    def _format_paper_info(self, paper: dict) -> str:
+        return f"**Title:** {paper.get('title', 'Unknown')}"
+
+# Usage
+storyteller = ScienceStoryteller()           # Create object (__init__ runs)
+result = await storyteller.process_topic("AlphaFold")  # Call method
+```
+
+### Quick Reference
+
+| Concept | Syntax | Purpose |
+|---------|--------|---------|
+| **Class** | `class ClassName:` | Blueprint for objects |
+| **Object** | `obj = ClassName()` | Instance created from class |
+| **Constructor** | `def __init__(self):` | Initialize object state |
+| **Self** | `self.attribute` | Reference to current object |
+| **Attribute** | `self.name = value` | Data stored in object |
+| **Method** | `def method(self, args):` | Function belonging to class |
+| **Public** | `def method(self):` | External API |
+| **Private** | `def _method(self):` | Internal helper |
+
+---
+
+## Async/Await Deep Dive
+
+### Why Async? The Three Use Cases
+
+Based on [RealPython's async guide](https://realpython.com/async-io-python/):
+
+1. **Writing pausable/resumable functions**
+2. **Managing I/O-bound tasks** (network, files, databases)
+3. **Improving performance** (handle multiple tasks concurrently)
+
+**Science Storyteller uses all three!**
+
+### The Problem: Blocking I/O
+
+**Without async (blocking):**
+```python
+def process_topic_sync(topic):
+    papers = requests.get("arxiv_api")      # ⏸️ BLOCKS for 5 seconds
+    summary = requests.post("claude_api")   # ⏸️ BLOCKS for 10 seconds
+    audio = requests.post("elevenlabs_api") # ⏸️ BLOCKS for 60 seconds
+    return results  # Total: 75 seconds of BLOCKING
+
+# During blocking:
+# ❌ UI freezes
+# ❌ Progress bar can't update
+# ❌ Other users can't be served
+# ❌ Event loop is stuck
+```
+
+**With async (non-blocking):**
+```python
+async def process_topic(topic):
+    papers = await arxiv_tool.search()      # ⏸️ Yields control for 5 seconds
+    summary = await llm_tool.summarize()    # ⏸️ Yields control for 10 seconds
+    audio = await audio_tool.convert()      # ⏸️ Yields control for 60 seconds
+    return results  # Total: 75 seconds, but non-blocking
+
+# During await:
+# ✅ UI stays responsive
+# ✅ Progress bar updates
+# ✅ Other users can be served
+# ✅ Event loop continues running
+```
+
+### Visualizing Blocking vs. Async
+
+**Blocking (Sequential) Execution:**
+```
+Request 1:  [--arxiv--|----claude----|----------------audio----------------|]
+Request 2:                                                                [--arxiv--|----claude----|---...
+Time -----> 0s        5s           15s                                     75s       80s          90s
+```
+- The UI is frozen for the entire 75s duration of Request 1.
+- Request 2 must wait for Request 1 to completely finish.
+
+**Async (Concurrent) Execution:**
+```
+Request 1:  [--arxiv--] ... [----claude----] ... [----------------audio----------------]
+Request 2:    [--arxiv--] ...   [----claude----] ... [----------------audio----------------]
+Time -----> 0s        1s        5s           6s        15s           16s                   75s
+```
+- When Request 1 `await`s `arxiv`, the event loop is free to start Request 2.
+- Both requests run concurrently, sharing time during I/O waits. The UI remains responsive throughout.
+
+### How Async Works: The Event Loop
+
+```
+┌─────────────────────────────────────────┐
+│        Python Asyncio Event Loop        │
+│  (Single thread, multiple tasks)        │
+└─────────────────────────────────────────┘
+         ↓               ↓               ↓
+    Task A          Task B          Task C
+ (User 1 req)    (User 2 req)    (User 3 req)
+```
+
+**When `await` is hit:**
+1. Function **pauses** at that line
+2. Control **returns** to the event loop
+3. Event loop **runs other code** (updates UI, handles requests)
+4. When I/O completes, function **resumes** from where it paused
+
+### Single VM, Multiple Users
+
+**Key insight:** On Hugging Face Spaces, **all users share one Python process**.
+
+```
+Hugging Face Space (Single VM)
+├─ Python Process (port 7860)
+│  └─ Event Loop
+│     ├─ Task: User A (paused at await)
+│     ├─ Task: User B (paused at await)
+│     └─ Task: User C (paused at await)
+```
+
+**Without async (sequential):**
+```
+User A: 0-75s    (completes at 75s)
+User B: 75-150s  (WAITS 75s, then runs 75s = 150s total)
+User C: 150-225s (WAITS 150s, then runs 75s = 225s total)
+```
+
+**With async (concurrent):**
+```
+User A: 0-75s    (completes at 75s)
+User B: 1-76s    (starts 1s later, runs concurrently = 76s total)
+User C: 2-77s    (starts 2s later, runs concurrently = 77s total)
+```
+
+### Performance Comparison
+
+| Metric | Without Async | With Async |
+|--------|--------------|------------|
+| **User A wait** | 75s | 75s |
+| **User B wait** | 150s | ~76s |
+| **User C wait** | 225s | ~77s |
+| **UI responsiveness** | Frozen | Live updates |
+| **Progress tracking** | Can't update | Works |
+| **Concurrent users** | Sequential | Interleaved |
+
+### Gradio + Async Integration
+
+Gradio uses **FastAPI** internally, which is async-native:
+
+```python
+# Gradio internals (simplified)
+from fastapi import FastAPI
+
+app = FastAPI()
+
+@app.post("/api/predict")
+async def predict(request):
+    result = await your_gradio_function(request.data)
+    return result
+```
+
+**Why this matters:**
+- `gr.Progress()` only works with async (sends WebSocket updates)
+- Gradio's event loop can handle multiple users
+- Your async functions integrate seamlessly
+
+### Async Syntax Rules
+
+**Defining async functions:**
+```python
+async def my_function():  # Note the 'async' keyword
+    result = await some_async_operation()
+    return result
+```
+
+**Calling async functions:**
+```python
+# From another async function:
+result = await my_function()
+
+# From synchronous code:
+import asyncio
+result = asyncio.run(my_function())
+```
+
+**Common mistake:**
+```python
+# ❌ Wrong - missing await
+async def process():
+    result = some_async_function()  # This returns a coroutine, not the result!
+    
+# ✅ Correct - with await
+async def process():
+    result = await some_async_function()  # This waits and gets the actual result
+```
+
+### The Async Chain in Science Storyteller
+
+```
+app.py: process_topic (async)
+  ↓ await
+agents/research_agent.py: search (async)
+  ↓ await
+mcp_tools/arxiv_tool.py: search_papers (async)
+  ↓ await
+session.call_tool() (MCP I/O)
+  ↓
+[Network request to arXiv server]
+```
+
+**Every step must be async** because:
+- MCP communication uses async I/O
+- Can't `await` inside a non-async function
+- Event loop requires async all the way up
+
+---
+
+## Module-by-Module Learning Path
+
+### Level 1: Foundation (Start Here)
+
+#### 1. `utils/audio_processor.py`
+
+**What it does:** File system operations for audio files
+
+**Key concepts:**
+- Creating directories with `Path.mkdir()`
+- Checking file sizes with `os.path.getsize()`
+- Working with file paths
+
+**Learning exercise:**
+```python
+from utils.audio_processor import ensure_audio_dir, get_file_size_mb
+
+# Create the audio directory
+ensure_audio_dir()
+
+# Check size of a file (if it exists)
+# size = get_file_size_mb("assets/audio/podcast_123.mp3")
+```
+
+**What to look for:**
+- How does it handle file paths in a cross-platform way (`pathlib.Path`)?
+- The use of `exist_ok=True` to prevent errors.
+- Simple, pure functions that have no side effects other than interacting with the filesystem.
+
+**Questions to answer:**
+- Why use `Path` instead of strings for file paths?
+- What happens if the directory already exists?
+- How is file size converted from bytes to MB?
+
+---
+
+#### 2. `utils/script_formatter.py`
+
+**What it does:** Clean and format podcast scripts for TTS
+
+**Key concepts:**
+- String manipulation (`strip()`, `replace()`)
+- Regular expressions (if used)
+- Estimating audio duration from text
+
+**Learning exercise:**
+```python
+from utils.script_formatter import format_podcast_script, estimate_duration
+
+script = """
+Hello!   This is a test.
+
+With extra   spaces and newlines.
+"""
+
+cleaned = format_podcast_script(script)
+duration = estimate_duration(cleaned)
+
+print(f"Cleaned: {cleaned}")
+print(f"Duration: {duration} seconds")
+```
+
+**What to look for:**
+- How simple string methods (`.strip()`, `.replace()`) are used for cleaning.
+- The logic for `estimate_duration`: it's a heuristic, not an exact calculation.
+- This is another example of pure functions that are easy to test.
+
+**Questions to answer:**
+- How does text length relate to audio duration?
+- What characters need to be cleaned for TTS?
+- Why estimate duration before generating audio?
+
+---
+
+### Level 2: MCP Tools (Core Hackathon Requirement)
+
+#### 3. `mcp_tools/arxiv_tool.py`
+
+**What it does:** Connects to arXiv MCP server to search papers
+
+**Key concepts:**
+- Model Context Protocol (MCP)
+- Stdio transport (stdin/stdout communication)
+- Async context managers (`__aenter__`, `__aexit__`)
+- JSON-RPC messaging
+
+**Important code sections:**
+
+**Connection setup:**
+```python
+server_params = StdioServerParameters(
+    command="npx",
+    args=["-y", "@blindnotation/arxiv-mcp-server"],
+    env=None
+)
+
+self.exit_stack = stdio_client(server_params)
+stdio_transport = await self.exit_stack.__aenter__()
+read_stream, write_stream = stdio_transport
+self.session = ClientSession(read_stream, write_stream)
+await self.session.__aenter__()
+```
+
+**Calling tools:**
+```python
+result = await self.session.call_tool(
+    "search_arxiv",
+    {
+        "query": query,
+        "max_results": max_results,
+        "sort_by": sort_by
+    }
+)
+```
+
+**Learning exercise:**
+```python
+import asyncio
+from mcp_tools.arxiv_tool import ArxivTool
+
+async def explore_arxiv():
+    tool = ArxivTool()
+    
+    # Connect to MCP server
+    connected = await tool.connect()
+    print(f"Connected: {connected}")
+    
+    # Search for papers
+    papers = await tool.search_papers("quantum computing", max_results=3)
+    print(f"Found {len(papers)} papers:")
+    
+    for paper in papers:
+        print(f"\n  Title: {paper.get('title', 'N/A')}")
+        print(f"  Authors: {paper.get('authors', [])[:2]}")
+    
+    # Clean up
+    await tool.disconnect()
+
+asyncio.run(explore_arxiv())
+```
+
+**Questions to answer:**
+- What is stdio transport and why use it?
+- Why do we need both `exit_stack` and `session`?
+- What happens if the MCP server crashes?
+- How does `call_tool` send messages to the server?
+
+**Deep dive topics:**
+- JSON-RPC protocol format
+- Async context managers (what `__aenter__` and `__aexit__` do)
+- Process communication (pipes and streams)
+
+---
+
+#### 4. `mcp_tools/llm_tool.py`
+
+**What it does:** Calls Anthropic Claude API for summarization
+
+**Key concepts:**
+- HTTP API requests with async
+- Prompt engineering
+- API authentication
+- Response parsing
+
+**Important code sections:**
+
+**API call:**
+```python
+message = self.client.messages.create(
+    model=self.model,
+    max_tokens=max_tokens,
+    messages=[
+        {"role": "user", "content": prompt}
+    ]
+)
+
+summary = message.content[0].text
+```
+
+**Learning exercise:**
+```python
+import asyncio
+from mcp_tools.llm_tool import LLMTool
+
+async def test_llm():
+    tool = LLMTool()  # Needs ANTHROPIC_API_KEY in .env
+    
+    # Fake paper data
+    paper = {
+        "title": "Quantum Computing Fundamentals",
+        "summary": "This paper explores the basic principles of quantum computing...",
+        "authors": [{"name": "Alice"}, {"name": "Bob"}]
+    }
+    
+    # Generate summary
+    summary = await tool.summarize_paper(paper, max_tokens=500)
+    print(f"Summary:\n{summary}")
+
+asyncio.run(test_llm())
+```
+
+**Questions to answer:**
+- How is the prompt structured for summarization?
+- What's the difference between `max_tokens` in the request and actual tokens used?
+- How does prompt engineering affect output quality?
+- What happens if the API returns an error?
+
+---
+
+### Level 3: Agents (Business Logic)
+
+#### 5. `agents/research_agent.py`
+
+**What it does:** Autonomous paper retrieval and search optimization
+
+**Key concepts:**
+- Query enhancement (autonomous planning)
+- Fallback strategies (self-correction)
+- Agent initialization and cleanup
+
+**Autonomous behaviors:**
+```python
+def _enhance_query(self, topic: str) -> str:
+    """
+    Autonomous planning - agent decides how to optimize search.
+    """
+    topic_lower = topic.lower()
+    
+    enhancements = {
+        'ai': 'artificial intelligence machine learning',
+        'ml': 'machine learning',
+        'quantum': 'quantum computing physics',
+    }
+    
+    for key, value in enhancements.items():
+        if key in topic_lower and value not in topic_lower:
+            return f"{topic} {value}"
+    
+    return topic
+```
+
+**Self-correction:**
+```python
+papers = await self.arxiv_tool.search_papers(enhanced_query)
+
+if not papers:
+    # Fallback: try original query
+    papers = await self.arxiv_tool.search_papers(topic)
+```
+
+**Learning exercise:**
+```python
+from agents.research_agent import ResearchAgent
+
+async def test_research():
+    agent = ResearchAgent()
+    await agent.initialize()
+    
+    # Test query enhancement
+    original = "AI"
+    enhanced = agent._enhance_query(original)
+    print(f"Original: {original}")
+    print(f"Enhanced: {enhanced}")
+    
+    # Test search
+    papers = await agent.search("AlphaFold", max_results=3)
+    print(f"\nFound {len(papers)} papers")
+    
+    await agent.cleanup()
+
+asyncio.run(test_research())
+```
+
+**Questions to answer:**
+- Why enhance queries? What problem does it solve?
+- When should you use the fallback strategy?
+- Why initialize and cleanup separately from `__init__`?
+
+---
+
+#### 6. `agents/analysis_agent.py`
+
+**What it does:** Paper analysis and podcast script generation
+
+**Key concepts:**
+- Paper selection (reasoning)
+- LLM-based summarization
+- Script generation with prompt engineering
+- Fallback content for LLM failures
+
+**Autonomous reasoning:**
+```python
+async def select_best(self, papers: list, topic: str):
+    """
+    Reasoning - evaluate and select most relevant paper.
+    """
+    scored_papers = []
+    for paper in papers:
+        score = 0
+        
+        # Has abstract
+        if paper.get('summary') or paper.get('abstract'):
+            score += 1
+        
+        # Recent paper
+        pub_date = paper.get('published', '')
+        if '2024' in pub_date or '2023' in pub_date:
+            score += 2
+        
+        scored_papers.append((score, paper))
+    
+    scored_papers.sort(key=lambda x: x[0], reverse=True)
+    return scored_papers[0][1] if scored_papers else papers[0]
+```
+
+**Learning exercise:**
+```python
+from agents.analysis_agent import AnalysisAgent
+
+async def test_analysis():
+    agent = AnalysisAgent()
+    
+    # Mock paper data
+    papers = [
+        {"title": "Old Paper", "published": "2020-01-01", "summary": "..."},
+        {"title": "New Paper", "published": "2024-01-01", "summary": "..."},
+    ]
+    
+    best = await agent.select_best(papers, "quantum computing")
+    print(f"Selected: {best['title']}")
+
+asyncio.run(test_analysis())
+```
+
+**Questions to answer:**
+- What criteria determine "best" paper?
+- Why fallback to template content instead of failing?
+- How does prompt engineering affect script quality?
+
+---
+
+#### 7. `agents/audio_agent.py`
+
+**What it does:** Text-to-speech conversion via ElevenLabs
+
+**Key concepts:**
+- HTTP POST with binary response
+- File I/O (saving MP3 bytes)
+- API timeout handling
+- Voice configuration
+
+**Learning exercise:**
+```python
+from agents.audio_agent import AudioAgent
+
+async def test_audio():
+    agent = AudioAgent()  # Needs ELEVENLABS_API_KEY
+    
+    script = "Welcome to Science Storyteller. Today we explore quantum computing."
+    
+    audio_path = await agent.text_to_speech(script)
+    
+    if audio_path:
+        print(f"Audio saved to: {audio_path}")
+    else:
+        print("Audio generation failed")
+
+asyncio.run(test_audio())
+```
+
+**Questions to answer:**
+- Why does TTS take so long (30-60 seconds)?
+- What happens if the API times out?
+- How are MP3 bytes different from text?
+
+---
+
+### Level 4: Orchestration (Integration)
+
+#### 8. `app.py` - `ScienceStoryteller` Class
+
+**What it does:** Coordinates all agents into a complete workflow
+
+**Key concepts:**
+- Orchestrator pattern
+- Error recovery
+- Progress tracking
+- State management
+
+**Learning exercise:**
+```python
+from app import ScienceStoryteller
+
+async def test_orchestrator():
+    storyteller = ScienceStoryteller()
+    
+    # Test full workflow
+    result = await storyteller.process_topic("quantum entanglement")
+    summary, script, audio, paper_info, status = result
+    
+    print(f"Status: {status}")
+    if summary:
+        print(f"Summary length: {len(summary)} chars")
+
+asyncio.run(test_orchestrator())
+```
+
+**Questions to answer:**
+- How does the orchestrator handle partial failures?
+- Why return a tuple instead of a dict?
+- What's the role of `gr.Progress()`?
+
+---
+
+#### 9. `app.py` - Gradio Interface
+
+**What it does:** Web UI for user interaction
+
+**Key concepts:**
+- Gradio Blocks API
+- Event handlers
+- Async in Gradio
+- UI layout
+
+**Learning exercise:**
+```python
+# Just run the app
+python app.py
+
+# Then interact with the UI to see the flow
+```
+
+**Questions to answer:**
+- How does Gradio handle async functions?
+- What's the difference between `gr.Blocks` and `gr.Interface`?
+- How are outputs mapped to UI components?
+
+---
+
+## Hands-On Exercises
+
+### Exercise 1: Test Individual Tools
+
+**Goal:** Verify MCP connection works
+
+```python
+# File: test_my_learning.py
+import asyncio
+from mcp_tools.arxiv_tool import ArxivTool
+
+async def main():
+    print("Testing ArxivTool...")
+    
+    tool = ArxivTool()
+    connected = await tool.connect()
+    
+    if connected:
+        print("✓ Connected to MCP server")
+        
+        papers = await tool.search_papers("AlphaFold", max_results=2)
+        print(f"✓ Found {len(papers)} papers")
+        
+        for i, paper in enumerate(papers, 1):
+            print(f"\n{i}. {paper.get('title', 'N/A')}")
+        
+        await tool.disconnect()
+        print("\n✓ Disconnected")
+    else:
+        print("✗ Failed to connect")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+Run: `python test_my_learning.py`
+
+---
+
+### Exercise 2: Trace the Async Chain
+
+**Goal:** Understand how async calls propagate
+
+Add print statements to trace execution:
+
+```python
+# In arxiv_tool.py
+async def search_papers(self, query: str, ...):
+    print(f"[ArxivTool] Starting search for: {query}")
+    result = await self.session.call_tool("search_arxiv", {...})
+    print(f"[ArxivTool] Search complete, parsing results...")
+    return papers
+
+# In research_agent.py
+async def search(self, topic: str, max_results: int = 5):
+    print(f"[ResearchAgent] Enhancing query: {topic}")
+    enhanced = self._enhance_query(topic)
+    print(f"[ResearchAgent] Enhanced to: {enhanced}")
+    papers = await self.arxiv_tool.search_papers(enhanced)
+    print(f"[ResearchAgent] Got {len(papers)} papers")
+    return papers
+```
+
+Then run and watch the flow!
+
+---
+
+### Exercise 3: Mock External Dependencies
+
+**Goal:** Test without API keys
+
+```python
+# test_mock.py
+from unittest.mock import AsyncMock, Mock
+from agents.research_agent import ResearchAgent
+
+async def test_with_mock():
+    agent = ResearchAgent()
+    
+    # Mock the arxiv_tool to avoid real API calls
+    agent.arxiv_tool.search_papers = AsyncMock(return_value=[
+        {"title": "Fake Paper 1", "summary": "Test"},
+        {"title": "Fake Paper 2", "summary": "Test"},
+    ])
+    
+    papers = await agent.search("test topic")
+    
+    assert len(papers) == 2
+    print(f"✓ Mock test passed: {len(papers)} papers")
+
+asyncio.run(test_with_mock())
+```
+
+---
+
+### Exercise 4: Build a Mini Version
+
+**Goal:** Understand the workflow by simplifying
+
+```python
+# mini_storyteller.py
+import asyncio
+
+class MiniStoryteller:
+    """Simplified version to understand the flow"""
+    
+    def __init__(self):
+        print("📚 Initializing agents...")
+        self.research = "ResearchAgent"
+        self.analysis = "AnalysisAgent"
+        self.audio = "AudioAgent"
+    
+    async def process(self, topic):
+        print(f"\n🔍 Step 1: Search for '{topic}'")
+        await asyncio.sleep(1)  # Simulate API call
+        papers = ["Paper 1", "Paper 2"]
+        
+        print(f"📝 Step 2: Select best paper")
+        await asyncio.sleep(1)
+        best = papers[0]
+        
+        print(f"✍️ Step 3: Summarize '{best}'")
+        await asyncio.sleep(1)
+        summary = "This is a summary..."
+        
+        print(f"🎙️ Step 4: Generate script")
+        await asyncio.sleep(1)
+        script = "Welcome to the podcast..."
+        
+        print(f"🔊 Step 5: Convert to audio")
+        await asyncio.sleep(2)
+        audio = "podcast.mp3"
+        
+        print(f"✅ Done!")
+        return summary, script, audio
+
+async def main():
+    storyteller = MiniStoryteller()
+    result = await storyteller.process("AlphaFold")
+    print(f"\nResult: {result}")
+
+asyncio.run(main())
+```
+
+---
+
+## Common Patterns Explained
+
+### Pattern 1: Async Context Managers
+
+**What you see:**
+```python
+self.exit_stack = stdio_client(server_params)
+stdio_transport = await self.exit_stack.__aenter__()
+# ... use the connection ...
+await self.exit_stack.__aexit__(None, None, None)
+```
+
+**What it means:**
+- `__aenter__`: Setup (open connection, allocate resources)
+- `__aexit__`: Cleanup (close connection, free resources)
+
+**Better syntax:**
+```python
+async with stdio_client(server_params) as stdio_transport:
+    # Connection is open here
+    read_stream, write_stream = stdio_transport
+    # ... use streams ...
+# Connection automatically closed when block exits
+```
+
+**Why the manual version in the code?**
+- Need to keep connection alive for multiple operations
+- Can't use `async with` because connection persists beyond one function call
+
+---
+
+### Pattern 2: Optional Parameters with Defaults
+
+```python
+async def search(self, topic: str, max_results: int = 5):
+    """Search with default max_results"""
+```
+
+**Usage:**
+```python
+# Use default
+papers = await agent.search("AI")  # max_results=5
+
+# Override default  
+papers = await agent.search("AI", max_results=10)
+```
+
+---
+
+### Pattern 3: Type Hints
+
+```python
+async def search_papers(
+    self,
+    query: str,                    # Must be a string
+    max_results: int = 5,          # Must be an int, defaults to 5
+    sort_by: str = "relevance"     # Must be a string, defaults to "relevance"
+) -> List[Dict[str, Any]]:         # Returns a list of dictionaries
+```
+
+**Benefits:**
+- Self-documenting code
+- IDE autocomplete
+- Type checking tools (mypy)
+- Easier to catch bugs
+
+---
+
+### Pattern 4: Dictionary `.get()` with Defaults
+
+```python
+title = paper.get('title', 'Unknown')  # Returns 'Unknown' if 'title' key missing
+```
+
+**Why not just `paper['title']`?**
+- `paper['title']` → Raises `KeyError` if missing
+- `paper.get('title', 'Unknown')` → Returns default if missing (safer)
+
+---
+
+### Pattern 5: List Comprehension
+
+```python
+author_names = [
+    author.get('name', '')
+    for author in authors[:5]
+    if isinstance(author, dict)
+]
+```
+
+**Equivalent to:**
+```python
+author_names = []
+for author in authors[:5]:
+    if isinstance(author, dict):
+        author_names.append(author.get('name', ''))
+```
+
+---
+
+### Pattern 6: Try/Except for Error Handling
+
+```python
+try:
+    result = await api_call()
+    return result
+except Exception as e:
+    logger.error(f"API error: {e}")
+    return fallback_result()
+```
+
+**Why?**
+- External APIs can fail
+- Network can be unreliable
+- Graceful degradation instead of crashes
+
+---
+
+## Debugging Tips
+
+### Tip 1: Use Print Debugging
+
+Add strategic print statements:
+
+```python
+async def search(self, topic: str):
+    print(f"🔍 [DEBUG] Searching for: {topic}")
+    
+    enhanced = self._enhance_query(topic)
+    print(f"🔍 [DEBUG] Enhanced to: {enhanced}")
+    
+    papers = await self.arxiv_tool.search_papers(enhanced)
+    print(f"🔍 [DEBUG] Found {len(papers)} papers")
+    
+    return papers
+```
+
+---
+
+### Tip 2: Check Logs
+
+The app uses Python's logging:
+
+```python
+logging.basicConfig(
+    level=logging.INFO,  # Change to DEBUG for more detail
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+```
+
+Run with verbose logging:
+```bash
+python app.py 2>&1 | tee app.log
+```
+
+---
+
+### Tip 3: Use Python REPL
+
+Test small pieces interactively:
+
+```bash
+$ python
+>>> from utils.script_formatter import estimate_duration
+>>> text = "Hello world, this is a test."
+>>> duration = estimate_duration(text)
+>>> print(duration)
+5
+```
+
+---
+
+### Tip 4: Check Environment Variables
+
+```bash
+# Verify API keys are set
+echo $ANTHROPIC_API_KEY
+echo $ELEVENLABS_API_KEY
+
+# Or in Python
+import os
+print(os.getenv("ANTHROPIC_API_KEY"))
+```
+
+---
+
+### Tip 5: Test Error Cases
+
+```python
+# Test with invalid input
+result = await storyteller.process_topic("")  # Empty string
+result = await storyteller.process_topic("xyzinvalidtopic999")  # No results
+```
+
+---
+
+### Tip 6: Use Async Debugger
+
+For complex async issues:
+
+```python
+import asyncio
+asyncio.run(my_function(), debug=True)  # Enables debug mode
+```
+
+---
+
+## Further Resources
+
+### Official Documentation
+
+- **Python Async/Await**: [RealPython Guide](https://realpython.com/async-io-python/)
+- **MCP Protocol**: [Official Docs](https://modelcontextprotocol.io/)
+- **Anthropic Claude API**: [API Reference](https://docs.anthropic.com/claude/reference)
+- **Gradio**: [Documentation](https://www.gradio.app/docs)
+- **ElevenLabs**: [API Docs](https://elevenlabs.io/docs/api-reference)
+
+### Learning Paths
+
+**If you're new to async:**
+1. Read RealPython's async guide
+2. Practice with simple async examples
+3. Understand event loops
+4. Study this project's async chain
+
+**If you're new to OOP:**
+1. Python classes tutorial
+2. Understand `self` and `__init__`
+3. Practice with simple class examples
+4. Study `ScienceStoryteller` class
+
+**If you're new to MCP:**
+1. Read MCP specification
+2. Understand stdio transport
+3. Study `ArxivTool` implementation
+4. Try building your own MCP tool
+
+### Practice Projects
+
+**After understanding this codebase:**
+
+1. **Add a new MCP tool**: Try Semantic Scholar instead of arXiv
+2. **Add a new agent**: Create a fact-checking agent
+3. **Extend functionality**: Add multiple podcast voices
+4. **Improve error handling**: Better retry logic
+5. **Add caching**: Cache arXiv results for 24 hours
+
+---
+
+## Review Checklist
+
+Before moving on, can you answer:
+
+- [ ] What's the difference between a class and an object?
+- [ ] What does `self` refer to?
+- [ ] When does `__init__` run?
+- [ ] Why use `async`/`await`?
+- [ ] How does the event loop work?
+- [ ] What is MCP and why use it?
+- [ ] How do the three agents differ?
+- [ ] What does the orchestrator do?
+- [ ] How does Gradio integrate with async?
+- [ ] Where would you add error handling?
+- [ ] What is the difference between a unit and an integration test?
+
+---
+
+## Your Learning Journey
+
+**Recommended 3-Week Plan:**
+
+### Week 1: Fundamentals
+- Day 1-2: OOP basics (`__init__`, `self`, methods)
+- Day 3-4: Async/await concepts
+- Day 5-7: Study `utils/` and `mcp_tools/`
+
+### Week 2: Implementation
+- Day 8-10: Understand all three agents
+- Day 11-12: Study orchestrator
+- Day 13-14: Explore Gradio interface
+
+### Week 3: Integration & Polish
+- Day 15-17: Test full workflow
+- Day 18-19: Fix bugs, improve error handling
+- Day 20-21: Polish UI, prepare demo
+
+---
+
+**Remember:** Deep understanding takes time. Don't rush. Each module builds on the previous one. Master the basics before tackling integration!
+
+---
+
+**Last Updated:** November 17, 2025  
+**Version:** 1.0  
+**For:** MCP's 1st Birthday Hackathon 2025
+
+---
+
+## 🧪 Testing Strategy
+
+A good testing strategy is crucial for building reliable software. For this project, we can use a model called the "Testing Pyramid."
+
+### Unit Tests
+
+**Definition:** Test individual components in isolation.
+
+- **What to test:** Pure functions, methods with no external dependencies.
+- **Tools:** Python's built-in `unittest` or `pytest`.
+- **Example:**
+    ```python
+    import unittest
+
+    class TestArxivTool(unittest.TestCase):
+        def test_search_papers(self):
+            tool = ArxivTool()
+            result = asyncio.run(tool.search_papers("AI"))
+            self.assertGreater(len(result), 0)
+    ```
+
+### Integration Tests
+
+**Definition:** Test how components work together.
+
+- **What to test:** Interactions between modules, like agent and tool communication.
+- **Tools:** `pytest` with async support.
+- **Example:**
+    ```python
+    async def test_agent_tool_integration():
+        agent = ResearchAgent()
+        await agent.initialize()
+        
+        papers = await agent.search("AI")
+        self.assertIsInstance(papers, list)
+        self.assertGreater(len(papers), 0)
+    ```
+
+### End-to-End Tests
+
+**Definition:** Test the complete workflow from start to finish.
+
+- **What to test:** User scenarios, like submitting a topic and receiving audio.
+- **Tools:** Gradio's built-in testing, Selenium for UI tests.
+- **Example:**
+    ```python
+    def test_gradio_interface(client):
+        response = client.post("/api/predict", json={"data": "AI in healthcare"})
+        assert response.status_code == 200
+        assert "audio" in response.json()
+    ```
+
+### Load Tests
+
+**Definition:** Test system behavior under heavy load.
+
+- **What to test:** How the system handles many requests at once.
+- **Tools:** Locust, JMeter.
+- **Example:**
+    ```
+    locust -f load_test.py
+    ```
+
+### Security Tests
+
+**Definition:** Identify vulnerabilities in the application.
+
+- **What to test:** API security, data validation, authentication.
+- **Tools:** OWASP ZAP, Burp Suite.
+- **Example:**
+    ```
+    zap-cli quick-scan --self-contained --spider -r http://localhost:7860
+    ```
+
+### Best Practices
+
+- **Automate tests**: Use CI/CD pipelines to run tests automatically.
+- **Test coverage**: Aim for at least 80% coverage, but prioritize critical paths.
+- **Mock external services**: Use tools like `vcr.py` or `responses` to mock API calls.
+- **Data-driven tests**: Use parameterized tests to cover multiple scenarios.
+- **Regularly review and update tests**: As the code evolves, so should the tests.
+
+---