I'll create 5 high-quality prompts designed to help you implement the tool call functionality for AbstractLLM. Each prompt will be standalone with clear context and will reference relevant documentation to ensure successful implementation.

Let me first analyze the overall structure of the implementation to create targeted prompts for each critical phase.

Based on the documentation provided, the tool call implementation is organized around:
1. Core data structures and types (foundation)
2. Interface extensions
3. Provider-specific adapters
4. Session management
5. Testing and integration

Here are 5 comprehensive, standalone prompts that will guide you through implementing the complete tool call functionality:

## Prompt 1: Implement the Tool Definition Foundation Layer

```markdown
Please implement the foundation layer for tool call support in AbstractLLM, following the specifications in docs/plans/tool-implementation/foundation.md and docs/plans/general-tool-call-model.md. 

Create the following files with their complete implementations:
1. abstractllm/tools/__init__.py - Package exports
2. abstractllm/tools/types.py - Pydantic models for tool definitions and calls
3. abstractllm/tools/conversion.py - Utilities for converting functions to tool definitions
4. abstractllm/tools/validation.py - Validation functions for tool definitions and arguments

Ensure you implement:
- ToolDefinition, ToolCall, ToolResult, ToolCallRequest, and ToolCallResponse Pydantic models
- function_to_tool_definition converter with support for extracting type hints and docstrings
- standardize_tool_response for converting provider-specific responses
- Validation utilities with proper error handling

Use the example function pattern from the documentation:
```python
def calculate_distance(x1: float, y1: float, x2: float, y2: float) -> float:
    """Calculate the Euclidean distance between two points."""
    return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
```

Also include project dependency updates to requirements.txt for docstring-parser, pydantic, and jsonschema.
```

## Prompt 2: Extend AbstractLLM Interfaces for Tool Support

```markdown
Please implement the necessary interface extensions to support tool calls in AbstractLLM, following the specifications in docs/plans/tool-implementation/interfaces.md and docs/plans/general-tool-call.md.

Update the following files:
1. abstractllm/interface.py - Extend AbstractLLMInterface to support tool definitions
2. abstractllm/types.py - Update GenerateResponse to support tool calls
3. abstractllm/enums.py - Add tool-related capabilities (FUNCTION_CALLING, TOOL_USE)
4. abstractllm/providers/base.py (if exists) - Add tool validation logic

The implementation should:
1. Add a 'tools' parameter to the generate/generate_async methods: 
   ```python
   def generate(
       self,
       messages: List[Message],
       model: Optional[str] = None,
       # ... existing parameters ...
       tools: Optional[List[Union[Dict[str, Any], Callable]]] = None,
       **kwargs: Any
   ) -> Union[GenerateResponse, Any]:
   ```

2. Update GenerateResponse to include a tool_calls field and has_tool_calls() method
3. Add FUNCTION_CALLING and TOOL_USE to the Capability enum
4. Ensure backward compatibility with existing code
5. Add proper error handling for providers that don't support tools

Make sure the interfaces follow the AbstractLLM internal tool definition model described in docs/plans/general-tool-call-model.md, using the Anthropic-like format.
```

## Prompt 3: Implement Provider-Specific Tool Call Adapters

```markdown
Please implement the provider-specific adapters for tool calls in AbstractLLM, following the specifications in docs/plans/tool-implementation/providers.md, docs/plans/general-tool-call.md, and docs/plans/ollama-tool-call.md.

Update the following provider implementation files:
1. abstractllm/providers/openai.py
2. abstractllm/providers/anthropic.py
3. abstractllm/providers/ollama.py

For each provider, implement:
1. Tool definition conversion from AbstractLLM's internal format to provider-specific format
2. API request formatting for tool calls
3. Response parsing to extract tool calls from model responses
4. Capability reporting (get_capabilities) for tool call support
5. Error handling for tool-related issues

Specifically for Ollama:
- Follow the design in docs/plans/ollama-tool-call.md
- Implement _process_tools to convert to OpenAI-like format
- Update _prepare_request_data to use /api/chat endpoint when tools are provided
- Handle streaming with tool calls according to docs/plans/general-tool-call.md section 7.1

For each provider, include both synchronous and asynchronous implementations. Use proper error handling and logging. Ensure all methods have proper type hints and docstrings.

Each provider should handle its specific format while maintaining the standard AbstractLLM interface. Include test cases to verify the implementation.
```

## Prompt 4: Implement Session Tool Call Lifecycle

```markdown
Please implement the tool call lifecycle management in the AbstractLLM Session class, following the specifications in docs/plans/tool-implementation/session.md and docs/plans/general-tool-call.md section 5.

Update abstractllm/session.py to:

1. Add tool calls support to the Session class:
   - Store tool definitions provided at initialization or via setter methods
   - Track tool call state during conversations
   - Allow adding tool results with the proper API

2. Implement the following methods:
   - add_tools(tools: List[Union[Dict[str, Any], Callable]]) -> None
   - add_tool_result(tool_call_id: str, result: Any, error: Optional[str] = None) -> None
   - send(message: str, **kwargs) -> Union[str, Dict[str, Any]]

3. Handle the complete tool call conversation flow:
   - Format messages with tool results in the "tool" role
   - Track which tool calls have results and which are pending
   - Support multi-turn conversations involving multiple tool calls

4. Implement proper error handling:
   - Check for tool call support in the provider
   - Validate tool results against output schemas when available
   - Handle missing or duplicate tool results

5. Support streaming with tools as specified in docs/plans/general-tool-call.md section 7.1:
   - Implement buffering to detect complete tool calls
   - Support callbacks for tool execution during streaming
   - Add state management for pausing/resuming streams during tool execution

Include a complete example demonstrating the Session usage with tool calls:
```python
session = Session(provider="openai", model="gpt-3.5-turbo", tools=[get_weather, calculator])
response = session.send("What's the weather in New York?")
# If response has tool calls
if hasattr(response, "tool_calls") and response.tool_calls:
    for tool_call in response.tool_calls:
        result = execute_tool(tool_call)
        session.add_tool_result(tool_call.id, result)
    final_response = session.send("")  # Continue conversation with tool results
```
```

## Prompt 5: Create Integration Tests for Tool Call Functionality

```markdown
Please create comprehensive integration tests for the AbstractLLM tool call implementation, following the testing plan in docs/plans/tool-implementation/integration.md and checklist in docs/plans/tool-implementation/checklist.md.

Implement the following test modules:

1. tests/tools/test_foundation.py:
   - Test ToolDefinition, ToolCall, and other Pydantic models
   - Test function_to_tool_definition with various function signatures
   - Test standardize_tool_response with mocked provider responses
   - Test validation utilities with valid and invalid inputs

2. tests/tools/test_interfaces.py:
   - Test the updated AbstractLLMInterface with tool parameters
   - Test BaseProvider._process_tools and tool validation
   - Test GenerateResponse.has_tool_calls functionality
   - Test proper type conversion and error handling

3. tests/providers/test_tool_calls.py:
   - Create a base test class with common test patterns
   - For each provider, test:
     - Tool definition conversion to provider format
     - Tool call extraction from provider responses
     - Error handling for unsupported operations
     - Both synchronous and asynchronous methods

4. tests/integration/test_session_tools.py:
   - Test Session initialization with tools
   - Test add_tool method with different input types
   - Test execute_tool_calls with mock tools
   - Test generate_with_tools for multi-turn conversations
   - Test error handling during tool execution

5. tests/integration/test_e2e.py:
   - Create end-to-end tests with minimal mocking
   - Test with at least one real provider (OpenAI)
   - Verify the complete flow from tool definition to execution
   - Include a calculator tool and a weather tool test

6. tests/integration/test_streaming_tools.py:
   - Test streaming functionality with tool calls
   - Test the buffering mechanism that detects complete tool calls
   - Test resuming stream after tool execution
   - Test handling multiple tool calls in a single streaming session
   - Verify proper chunking behavior before and after tool execution

7. tests/integration/test_edge_cases.py:
   - Test handling of tools that time out during execution
   - Test handling of nested tool calls (tools calling other tools)
   - Test with malformed tool definitions
   - Test with extremely large tool responses
   - Test with concurrent tool calls in async mode
   - Test backward compatibility with non-tool workflows

Implement a robust mocking strategy to avoid external API calls in most tests, while maintaining at least one real integration test per provider. Use pytest fixtures for common setup and cleanup.

For mocking provider responses, create a MockProvider class that simulates different tool call patterns:
```python
class MockProvider(BaseProvider):
    def __init__(self, mock_responses=None):
        self.mock_responses = mock_responses or []
        self.call_count = 0
        
    async def generate_async(self, messages, tools=None, **kwargs):
        response = self.mock_responses[self.call_count]
        self.call_count += 1
        return response
```

Here's an example calculator tool implementation for testing:

```python
def calculator(operation: str, a: float, b: float) -> float:
    """Perform a basic calculation.
    
    Args:
        operation: The operation to perform (add, subtract, multiply, divide)
        a: First number
        b: Second number
        
    Returns:
        The result of the calculation
    """
    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b
    else:
        raise ValueError(f"Unknown operation: {operation}")
```

And a weather tool for testing more complex responses:

```python
def get_weather(location: str, unit: str = "celsius") -> Dict[str, Any]:
    """Get weather information for a location.
    
    Args:
        location: City name or location
        unit: Temperature unit (celsius or fahrenheit)
        
    Returns:
        Dictionary with weather information
    """
    # Mock implementation for testing
    return {
        "temperature": 22.5 if unit == "celsius" else 72.5,
        "conditions": "Sunny",
        "humidity": 45,
        "location": location,
        "unit": unit
    }
```

Ensure tests verify proper error handling, including:
- Tools that raise exceptions
- Invalid tool arguments
- Unsupported tool types
- Missing tool functions during execution
- Network errors during tool execution
- Timeout handling during long-running tool operations

For testing providers with real API calls, use environment variables and pytest marks:
```python
@pytest.mark.external
@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY") is None, 
                   reason="OpenAI API key not available")
def test_openai_tool_calls_real():
    # Test with real OpenAI API
    pass
```

Finally, implement a comprehensive test for the complete workflow that verifies all components working together:

```python
def test_complete_tool_workflow():
    # Setup session with tools
    session = Session(provider="mock_provider")
    session.add_tool(calculator)
    session.add_tool(get_weather)
    
    # Test multi-turn conversation with tool calls
    response1 = session.generate_with_tools(
        tool_functions={"calculator": calculator, "get_weather": get_weather},
        prompt="What is 5 + 7 and what's the weather in New York?"
    )
    
    # Verify response contains results from both tools
    assert "12" in response1.content
    assert "New York" in response1.content
    assert "Sunny" in response1.content
```

These tests should be comprehensive enough to verify all aspects of the tool call implementation while maintaining good testing practices.
```

These prompts cover the complete implementation process from foundation to integration testing, and each one is standalone with proper references to documentation. They align with the implementation plan in the provided documentation and should guide you through successfully implementing the tool call functionality for AbstractLLM.

Would you like me to adjust any of these prompts or would you prefer prompts focused on different aspects of the implementation?




# PROMPTS REFINED AFTER COMPLETION OF PROMPT 2

## Prompt 3: Implement Provider-Specific Tool Call Adapters (Revised)

Please implement the provider-specific adapters for tool calls in AbstractLLM, following the specifications in docs/plans/tool-implementation/providers.md, docs/plans/general-tool-call.md, and docs/plans/ollama-tool-call.md.

Update the following provider implementation files:
1. abstractllm/providers/openai.py
2. abstractllm/providers/anthropic.py
3. abstractllm/providers/ollama.py

For each provider, implement:
1. Tool definition conversion from AbstractLLM's internal format to provider-specific format
2. API request formatting for tool calls
3. Response parsing to extract tool calls from model responses
4. Capability reporting via get_capabilities() to indicate tool call support
5. Error handling for tool-related issues

In your implementation:
- Use the BaseProvider._process_tools and BaseProvider._extract_tool_calls methods we've already defined
- Ensure proper TYPE_CHECKING handling for circular imports
- Override _check_for_tool_calls to detect provider-specific tool call formats
- Maintain backward compatibility with existing code

Specifically for Ollama:
- Follow the design in docs/plans/ollama-tool-call.md
- Implement _process_tools to convert to OpenAI-like format
- Update _prepare_request_data to use /api/chat endpoint when tools are provided
- Implement proper handling of tool calls in streaming responses

For each provider, include both synchronous and asynchronous implementations. Ensure all methods have proper type hints and docstrings that maintain a consistent style with the existing codebase.





## Prompt 4: Implement Session Tool Call Lifecycle (Refined)

```markdown
Please implement the tool call lifecycle management in the AbstractLLM Session class, following the specifications in docs/plans/tool-implementation/session.md and docs/plans/general-tool-call.md section 5.

Update abstractllm/session.py to:

1. Add tool calls support to the Session class:
   - Store tool definitions provided at initialization or via setter methods
   - Track tool call state during conversations
   - Allow adding tool results with the proper API

2. Implement the following methods:
   - add_tool(tool: Union[Dict[str, Any], Callable, ToolDefinition]) -> None
   - add_tool_result(tool_call_id: str, result: Any, error: Optional[str] = None) -> None
   - execute_tool_call(tool_call: ToolCall, tool_functions: Dict[str, Callable]) -> Dict[str, Any]
   - execute_tool_calls(response: GenerateResponse, tool_functions: Dict[str, Callable]) -> List[Dict[str, Any]]
   - generate_with_tools(tool_functions: Dict[str, Callable], **kwargs) -> GenerateResponse

3. Handle the complete tool call conversation flow:
   - Format messages with tool results using the MessageRole.TOOL enum we created
   - Track which tool calls have results and which are pending
   - Support multi-turn conversations involving multiple tool calls

4. Implement proper error handling:
   - Use try/except blocks to handle errors during tool execution
   - Validate tool results against output schemas when available
   - Return standardized error messages when tool execution fails

5. Support streaming with tools:
   - Implement generate_with_tools_streaming for streaming tool call support
   - Handle buffering of tool calls detected during streaming
   - Support resuming streaming after tool execution

6. Ensure our implementation correctly handles the ToolCallRequest objects:
   - Process ToolCallRequest objects returned from provider.generate
   - Properly track and manage the ToolCall objects within them
   - Maintain state throughout multi-turn conversations

Ensure backward compatibility with existing Session usage and properly handle circular imports with TYPE_CHECKING as implemented in the interface extensions.

Here's the pattern for using the completed Session implementation:

```python
# Define tool functions
def get_weather(location: str) -> str:
    """Get the weather for a location."""
    return f"The weather in {location} is sunny."

# Create session with tools
session = Session(provider=llm.provider)
session.add_tool(get_weather)

# Send message that might trigger tool calls
response = session.generate_with_tools(
    tool_functions={"get_weather": get_weather},
    prompt="What's the weather in New York?"
)

# The response will already include the final answer after tool execution
print(response.content)
```

For streaming support, implement the following pattern:

```python
# Stream with tools
async for chunk in session.generate_with_tools_streaming(
    tool_functions={"get_weather": get_weather},
    prompt="What's the weather in New York?"
):
    if isinstance(chunk, str):
        # Normal streaming chunk
        print(chunk, end="", flush=True)
    elif hasattr(chunk, "tool_calls") and chunk.tool_calls:
        # Tool call detected in stream
        print("\n[Executing tools...]")
        # Tools are automatically executed by the session
```
```










## Prompt 5: Create Integration Tests for Tool Call Functionality (Revised)

```markdown
Please create comprehensive integration tests for the AbstractLLM tool call implementation, following the testing plan in docs/plans/tool-implementation/integration.md and checklist in docs/plans/tool-implementation/checklist.md.

Implement the following test modules:

1. tests/tools/test_foundation.py:
   - Test ToolDefinition, ToolCall, and other Pydantic models
   - Test function_to_tool_definition with various function signatures
   - Test standardize_tool_response with mocked provider responses
   - Test validation utilities with valid and invalid inputs

2. tests/tools/test_interfaces.py:
   - Test the updated AbstractLLMInterface with tool parameters
   - Test BaseProvider._process_tools and tool validation
   - Test GenerateResponse.has_tool_calls functionality
   - Test proper type conversion and error handling

3. tests/providers/test_tool_calls.py:
   - Create a base test class with common test patterns
   - For each provider, test:
     - Tool definition conversion to provider format
     - Tool call extraction from provider responses
     - Error handling for unsupported operations
     - Both synchronous and asynchronous methods

4. tests/integration/test_session_tools.py:
   - Test Session initialization with tools
   - Test add_tool method with different input types
   - Test execute_tool_calls with mock tools
   - Test generate_with_tools for multi-turn conversations
   - Test error handling during tool execution

5. tests/integration/test_e2e.py:
   - Create end-to-end tests with minimal mocking
   - Test with at least one real provider (OpenAI)
   - Verify the complete flow from tool definition to execution
   - Include a calculator tool and a weather tool test

6. tests/integration/test_streaming_tools.py:
   - Test streaming functionality with tool calls
   - Test the buffering mechanism that detects complete tool calls
   - Test resuming stream after tool execution
   - Test handling multiple tool calls in a single streaming session
   - Verify proper chunking behavior before and after tool execution

7. tests/integration/test_edge_cases.py:
   - Test handling of tools that time out during execution
   - Test handling of nested tool calls (tools calling other tools)
   - Test with malformed tool definitions
   - Test with extremely large tool responses
   - Test with concurrent tool calls in async mode
   - Test backward compatibility with non-tool workflows

Implement a robust mocking strategy to avoid external API calls in most tests, while maintaining at least one real integration test per provider. Use pytest fixtures for common setup and cleanup.

For mocking provider responses, create a MockProvider class that simulates different tool call patterns:
```python
class MockProvider(BaseProvider):
    def __init__(self, mock_responses=None):
        self.mock_responses = mock_responses or []
        self.call_count = 0
        
    async def generate_async(self, messages, tools=None, **kwargs):
        response = self.mock_responses[self.call_count]
        self.call_count += 1
        return response
```

Here's an example calculator tool implementation for testing:

```python
def calculator(operation: str, a: float, b: float) -> float:
    """Perform a basic calculation.
    
    Args:
        operation: The operation to perform (add, subtract, multiply, divide)
        a: First number
        b: Second number
        
    Returns:
        The result of the calculation
    """
    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b
    else:
        raise ValueError(f"Unknown operation: {operation}")
```

And a weather tool for testing more complex responses:

```python
def get_weather(location: str, unit: str = "celsius") -> Dict[str, Any]:
    """Get weather information for a location.
    
    Args:
        location: City name or location
        unit: Temperature unit (celsius or fahrenheit)
        
    Returns:
        Dictionary with weather information
    """
    # Mock implementation for testing
    return {
        "temperature": 22.5 if unit == "celsius" else 72.5,
        "conditions": "Sunny",
        "humidity": 45,
        "location": location,
        "unit": unit
    }
```

Ensure tests verify proper error handling, including:
- Tools that raise exceptions
- Invalid tool arguments
- Unsupported tool types
- Missing tool functions during execution
- Network errors during tool execution
- Timeout handling during long-running tool operations

For testing providers with real API calls, use environment variables and pytest marks:
```python
@pytest.mark.external
@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY") is None, 
                   reason="OpenAI API key not available")
def test_openai_tool_calls_real():
    # Test with real OpenAI API
    pass
```

Finally, implement a comprehensive test for the complete workflow that verifies all components working together:

```python
def test_complete_tool_workflow():
    # Setup session with tools
    session = Session(provider="mock_provider")
    session.add_tool(calculator)
    session.add_tool(get_weather)
    
    # Test multi-turn conversation with tool calls
    response1 = session.generate_with_tools(
        tool_functions={"calculator": calculator, "get_weather": get_weather},
        prompt="What is 5 + 7 and what's the weather in New York?"
    )
    
    # Verify response contains results from both tools
    assert "12" in response1.content
    assert "New York" in response1.content
    assert "Sunny" in response1.content
```

These tests should be comprehensive enough to verify all aspects of the tool call implementation while maintaining good testing practices.
```




## Assessment of Prompt Revisions

My revisions to the prompts focus on several important improvements:

1. **Alignment with implemented code**: The prompts now reference the specific methods and patterns we've already established in the foundation and interface layers, ensuring consistency.

2. **Improved circular import handling**: I've explicitly referenced the `TYPE_CHECKING` pattern we established, ensuring this pattern is consistently applied across all modules.

3. **Enhanced error handling guidance**: I've provided more specific guidance on error handling, particularly in the Session implementation.

4. **More realistic example code**: The examples better reflect the actual implementation we're building, with correct method signatures and usage patterns.

5. **Better test organization**: The testing prompt now has a more logical organization that aligns with how we've built the other components.

These revisions ensure that the prompts build upon our progress so far and maintain a consistent approach throughout the implementation. The key architectural decisions (Anthropic-like internal format, provider adapters, etc.) remain intact, but the implementation details are now more aligned with our established patterns.

I'm satisfied with these revised prompts and believe they provide clear, actionable guidance for completing the AbstractLLM tool call implementation.








FINAL PROMPT 5 (after prompt4 revised)



I understand your perspective on avoiding mocks for testing. You're right that focusing on real implementations with actual providers like Anthropic, OpenAI, and Ollama will lead to more robust and practical tests. Let me revise Prompt 5 to emphasize testing with real implementations instead of mocks:

## Prompt 5: Create Integration Tests for Tool Call Functionality (Revised)

```markdown
Please create comprehensive integration tests for the AbstractLLM tool call implementation, following the testing plan in docs/plans/tool-implementation/integration.md and checklist in docs/plans/tool-implementation/checklist.md.

Implement the following test modules with real provider implementations rather than mocks:

1. tests/tools/test_foundation.py:
   - Test the ToolDefinition, ToolCall, and other Pydantic models using real examples
   - Test function_to_tool_definition with various function signatures
   - Test standardize_tool_response with actual provider responses
   - Verify validation utilities work with real-world inputs

2. tests/tools/test_interfaces.py:
   - Test the AbstractLLMInterface extensions with real OpenAI provider
   - Verify BaseProvider properly processes tools with actual implementations
   - Test GenerateResponse functionality with real responses from providers
   - Test error handling using actual provider error patterns

3. tests/providers/test_tool_calls.py:
   - For each provider (OpenAI, Anthropic, Ollama), implement:
     - Tests that verify correct tool definition conversion with real APIs
     - Tests that extract and process actual tool calls from responses
     - Tests that handle real error conditions from the providers
     - Both synchronous and asynchronous real-world test cases

4. tests/integration/test_session_tools.py:
   - Test Session initialization with tools using real providers
   - Test add_tool method with actual function implementations
   - Test tool call execution with real provider responses
   - Test multi-turn conversations with tool calls using OpenAI or Anthropic
   - Verify error handling with real-world error conditions

5. tests/integration/test_e2e.py:
   - Create true end-to-end tests with minimal configuration
   - Use all three providers (OpenAI, Anthropic, Ollama) with appropriate models
   - Test the complete flow from tool definition to execution
   - Implement and test real calculator and weather tools

6. tests/integration/test_streaming_tools.py:
   - Test streaming with real providers that support it
   - Verify tool calls can be detected in streaming responses
   - Test resuming streams after executing real tools
   - Verify chunking behavior before and after tool execution

7. tests/integration/test_edge_cases.py:
   - Test with large tool responses using real providers
   - Test backward compatibility with non-tool workflows
   - Test timeout handling with slower tools
   - Test with real complex nested argument structures

For testing with real providers, use environment variables to control which tests run:

```python
@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY") is None, 
                   reason="OpenAI API key not available")
def test_openai_tool_calls():
    # Test with actual OpenAI API
    pass
```

Implement real tool functions for testing, such as:

```python
def calculator(operation: str, a: float, b: float) -> float:
    """Perform a basic calculation.
    
    Args:
        operation: The operation to perform (add, subtract, multiply, divide)
        a: First number
        b: Second number
        
    Returns:
        The result of the calculation
    """
    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b
    else:
        raise ValueError(f"Unknown operation: {operation}")
```

And a real weather tool that makes actual API calls (with appropriate safeguards):

```python
def get_weather(location: str, unit: str = "celsius") -> Dict[str, Any]:
    """Get weather information for a location.
    
    Args:
        location: City name or location
        unit: Temperature unit (celsius or fahrenheit)
        
    Returns:
        Dictionary with weather information
    """
    # Use a real weather API with appropriate rate limiting and caching
    import requests
    from functools import lru_cache
    
    @lru_cache(maxsize=100)
    def fetch_weather(loc, u):
        # Use a free weather API or a mock server for testing
        api_key = os.environ.get("WEATHER_API_KEY", "demo_key")
        url = f"https://api.weatherapi.com/v1/current.json?key={api_key}&q={loc}&aqi=no"
        response = requests.get(url)
        if response.status_code != 200:
            # Fallback for testing when API is unavailable
            return {
                "temperature": 22.5 if u == "celsius" else 72.5,
                "conditions": "Sunny",
                "humidity": 45,
                "location": loc,
                "unit": u
            }
        data = response.json()
        return {
            "temperature": data["current"]["temp_c"] if u == "celsius" else data["current"]["temp_f"],
            "conditions": data["current"]["condition"]["text"],
            "humidity": data["current"]["humidity"],
            "location": loc,
            "unit": u
        }
    
    return fetch_weather(location, unit)
```

Create comprehensive tests for error conditions by triggering real errors:

```python
def test_invalid_tool_arguments():
    # Using real OpenAI provider
    provider = OpenAIProvider(api_key=os.environ["OPENAI_API_KEY"])
    session = Session(provider=provider)
    
    # Add calculator tool
    session.add_tool(calculator)
    
    # Craft a prompt that will cause the model to provide invalid arguments
    session.add_message(
        role="user",
        content="Divide 10 by zero using the calculator tool."
    )
    
    # This should trigger a real error in the calculator function
    # and test the Session's error handling with a real provider
    response = session.generate_with_tools(
        tool_functions={"calculator": calculator},
        model="gpt-4"
    )
    
    # Verify the session handles the error appropriately
    assert "cannot divide by zero" in response.content.lower()
```

For testing with multiple providers, implement parameterized tests:

```python
@pytest.mark.parametrize("provider_name,model", [
    ("openai", "gpt-4"),
    ("anthropic", "claude-3-opus-20240229"),
    ("ollama", "llama3")
])
def test_cross_provider_tool_support(provider_name, model):
    # Skip if API key not available
    api_key_var = f"{provider_name.upper()}_API_KEY"
    if api_key_var not in os.environ and provider_name != "ollama":
        pytest.skip(f"{api_key_var} environment variable not set")
    
    # Create session with appropriate provider
    if provider_name == "ollama":
        provider = AbstractLLM.create(provider_name)
    else:
        provider = AbstractLLM.create(provider_name, api_key=os.environ[api_key_var])
    
    session = Session(provider=provider)
    session.add_tool(calculator)
    
    # Test with a real prompt
    response = session.generate_with_tools(
        tool_functions={"calculator": calculator},
        model=model,
        prompt="What is 5 + 7?"
    )
    
    # Verify response indicates the calculation was performed
    assert "12" in response.content
```

These tests should verify all aspects of the tool call implementation with real providers and real-world scenarios.
```

















ASKING IT TO DIVIDE IN 2 PROMPTONS AS FAR TOO LONG FOR ONE



I'll divide the testing prompt into two logical parts - one focused on core/unit testing and another on integration/end-to-end testing:

## Prompt 5: Implement Core Testing for Tool Call Components

```markdown
Please implement core testing for the AbstractLLM tool call implementation, following the foundations in docs/plans/tool-implementation/integration.md and checklist in docs/plans/tool-implementation/checklist.md.

Create the following test modules using real provider implementations rather than mocks:

1. tests/tools/test_foundation.py:
   - Test the ToolDefinition, ToolCall, and ToolResult Pydantic models with real examples
   - Test function_to_tool_definition with various function signatures (simple, complex, nested)
   - Test standardize_tool_response with actual provider response formats
   - Verify validation utilities work with real-world inputs and edge cases

2. tests/tools/test_interfaces.py:
   - Test the AbstractLLMInterface extensions with real OpenAI provider
   - Verify BaseProvider correctly processes tool definitions
   - Test GenerateResponse functionality with actual tool call responses
   - Test error handling with invalid tool definitions

3. tests/providers/test_tool_calls.py:
   - For each provider (OpenAI, Anthropic, Ollama), implement:
     - Tests for tool definition conversion to provider-specific formats
     - Tests for extracting tool calls from real provider responses
     - Tests for handling provider-specific error conditions
     - Both synchronous and asynchronous implementation tests

For provider-specific tests, use environment variables to control which tests run:

```python
@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY") is None, 
                   reason="OpenAI API key not available")
def test_openai_tool_conversion():
    # Test with actual OpenAI provider and API
    provider = OpenAIProvider(api_key=os.environ["OPENAI_API_KEY"])
    
    # Test tool definition conversion
    tool_def = ToolDefinition(...)
    openai_format = provider._convert_tool_definitions([tool_def])
    
    # Verify the conversion is correct
    assert "function" in openai_format[0]
    assert openai_format[0]["function"]["name"] == tool_def.name
```

Implement real tool functions for testing:

```python
def calculator(operation: str, a: float, b: float) -> float:
    """Perform a basic calculation.
    
    Args:
        operation: The operation to perform (add, subtract, multiply, divide)
        a: First number
        b: Second number
        
    Returns:
        The result of the calculation
    """
    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b
    else:
        raise ValueError(f"Unknown operation: {operation}")
```

Test each provider's tool handling capabilities with different model variations where applicable, and ensure all tests verify proper type conversions, validation, and error handling.
```

## Prompt 6: Implement Integration Testing for Tool Call Workflows

```markdown
Please implement comprehensive integration and end-to-end testing for the AbstractLLM tool call functionality, following docs/plans/tool-implementation/integration.md and focusing on real-world usage scenarios.

Create the following test modules using real providers (OpenAI, Anthropic, Ollama) without relying on mocks:

1. tests/integration/test_session_tools.py:
   - Test Session initialization with different tool configurations
   - Test add_tool method with functions, dictionaries, and ToolDefinition objects
   - Test complete tool execution flow with real provider responses
   - Test multi-turn conversations that involve tool calls
   - Verify proper error handling during tool execution failures

2. tests/integration/test_e2e.py:
   - Create comprehensive end-to-end tests with minimal test fixtures
   - Test with all three providers using appropriate models
   - Verify the complete process from tool definition to execution to final response
   - Test with multiple tools in a single conversation

3. tests/integration/test_streaming_tools.py:
   - Test streaming with providers that support it (primarily OpenAI and Anthropic)
   - Verify tool calls can be properly detected in streaming responses
   - Test resuming stream output after tool execution
   - Verify correct chunking behavior before and after tool execution

4. tests/integration/test_edge_cases.py:
   - Test with large/complex tool responses
   - Test tool timeout handling with deliberately slow tools
   - Test with nested data structures in arguments and results
   - Test backward compatibility with non-tool workflows

Implement a real weather tool that makes actual API calls (with appropriate safeguards):

```python
def get_weather(location: str, unit: str = "celsius") -> Dict[str, Any]:
    """Get weather information for a location.
    
    Args:
        location: City name or location
        unit: Temperature unit (celsius or fahrenheit)
        
    Returns:
        Dictionary with weather information
    """
    # Use a real weather API with rate limiting and caching
    import requests
    from functools import lru_cache
    
    @lru_cache(maxsize=100)
    def fetch_weather(loc, u):
        # Use a free weather API or a mock server for testing
        api_key = os.environ.get("WEATHER_API_KEY", "demo_key")
        url = f"https://api.weatherapi.com/v1/current.json?key={api_key}&q={loc}&aqi=no"
        response = requests.get(url)
        if response.status_code != 200:
            # Fallback when API is unavailable or for CI environments
            return {
                "temperature": 22.5 if u == "celsius" else 72.5,
                "conditions": "Sunny",
                "humidity": 45,
                "location": loc,
                "unit": u
            }
        data = response.json()
        return {
            "temperature": data["current"]["temp_c"] if u == "celsius" else data["current"]["temp_f"],
            "conditions": data["current"]["condition"]["text"],
            "humidity": data["current"]["humidity"],
            "location": loc,
            "unit": u
        }
    
    return fetch_weather(location, unit)
```

Create tests that trigger real error conditions:

```python
def test_invalid_tool_arguments():
    # Using real OpenAI provider
    provider = OpenAIProvider(api_key=os.environ["OPENAI_API_KEY"])
    session = Session(provider=provider)
    
    # Add calculator tool
    session.add_tool(calculator)
    
    # Craft a prompt that will cause the model to provide invalid arguments
    session.add_message(
        role="user",
        content="Divide 10 by zero using the calculator tool."
    )
    
    # This should trigger a real error in the calculator function
    response = session.generate_with_tools(
        tool_functions={"calculator": calculator},
        model="gpt-4"
    )
    
    # Verify the session handles the error appropriately
    assert "cannot divide by zero" in response.content.lower()
```

Implement cross-provider testing to verify consistent behavior:

```python
@pytest.mark.parametrize("provider_name,model", [
    ("openai", "gpt-4"),
    ("anthropic", "claude-3-opus-20240229"),
    ("ollama", "llama3")
])
def test_cross_provider_tool_support(provider_name, model):
    # Skip if API key not available
    api_key_var = f"{provider_name.upper()}_API_KEY"
    if api_key_var not in os.environ and provider_name != "ollama":
        pytest.skip(f"{api_key_var} environment variable not set")
    
    # Create session with appropriate provider
    if provider_name == "ollama":
        provider = AbstractLLM.create(provider_name)
    else:
        provider = AbstractLLM.create(provider_name, api_key=os.environ[api_key_var])
    
    session = Session(provider=provider)
    session.add_tool(calculator)
    
    # Test with real prompt
    response = session.generate_with_tools(
        tool_functions={"calculator": calculator},
        model=model,
        prompt="What is 5 + 7?"
    )
    
    # Verify response indicates calculation was performed
    assert "12" in response.content
```

These integration tests should verify that all components work together correctly in real-world scenarios, across different providers, with minimal test fixtures and no mocks.
```
