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+---
+sidebar_position: 2
+---
+
+# Tools
+
+DSPy provides powerful support for **tool-using agents** that can interact with external functions, APIs, and services. Tools enable language models to go beyond text generation by performing actions, retrieving information, and processing data dynamically.
+
+There are two main approaches to using tools in DSPy:
+
+1. **`dspy.ReAct`** - A fully managed tool agent that handles reasoning and tool calls automatically
+2. **Manual tool handling** - Direct control over tool calls using `dspy.Tool`, `dspy.ToolCalls`, and custom signatures
+
+## Approach 1: Using `dspy.ReAct` (Fully Managed)
+
+The `dspy.ReAct` module implements the Reasoning and Acting (ReAct) pattern, where the language model iteratively reasons about the current situation and decides which tools to call.
+
+### Basic Example
+
+```python
+import dspy
+
+# Define your tools as functions
+def get_weather(city: str) -> str:
+    """Get the current weather for a city."""
+    # In a real implementation, this would call a weather API
+    return f"The weather in {city} is sunny and 75°F"
+
+def search_web(query: str) -> str:
+    """Search the web for information."""
+    # In a real implementation, this would call a search API
+    return f"Search results for '{query}': [relevant information...]"
+
+# Create a ReAct agent
+react_agent = dspy.ReAct(
+    signature="question -> answer",
+    tools=[get_weather, search_web],
+    max_iters=5
+)
+
+# Use the agent
+result = react_agent(question="What's the weather like in Tokyo?")
+print(result.answer)
+print("Tool calls made:", result.trajectory)
+```
+
+### ReAct Features
+
+- **Automatic reasoning**: The model thinks through the problem step by step
+- **Tool selection**: Automatically chooses which tool to use based on the situation
+- **Iterative execution**: Can make multiple tool calls to gather information
+- **Error handling**: Built-in error recovery for failed tool calls
+- **Trajectory tracking**: Complete history of reasoning and tool calls
+
+### ReAct Parameters
+
+```python
+react_agent = dspy.ReAct(
+    signature="question -> answer",  # Input/output specification
+    tools=[tool1, tool2, tool3],     # List of available tools
+    max_iters=10                     # Maximum number of tool call iterations
+)
+```
+
+## Approach 2: Manual Tool Handling
+
+For more control over the tool calling process, you can manually handle tools using DSPy's tool types.
+
+### Basic Setup
+
+```python
+import dspy
+
+class ToolSignature(dspy.Signature):
+    """Signature for manual tool handling."""
+    question: str = dspy.InputField()
+    tools: list[dspy.Tool] = dspy.InputField()
+    outputs: dspy.ToolCalls = dspy.OutputField()
+
+def weather(city: str) -> str:
+    """Get weather information for a city."""
+    return f"The weather in {city} is sunny"
+
+def calculator(expression: str) -> str:
+    """Evaluate a mathematical expression."""
+    try:
+        result = eval(expression)  # Note: Use safely in production
+        return f"The result is {result}"
+    except:
+        return "Invalid expression"
+
+# Create tool instances
+tools = {
+    "weather": dspy.Tool(weather),
+    "calculator": dspy.Tool(calculator)
+}
+
+# Create predictor
+predictor = dspy.Predict(ToolSignature)
+
+# Make a prediction
+response = predictor(
+    question="What's the weather in New York?", 
+    tools=list(tools.values())
+)
+
+# Execute the tool calls
+for call in response.outputs.tool_calls:
+    if call.name in tools:
+        result = tools[call.name](**call.args)
+        print(f"Tool: {call.name}")
+        print(f"Args: {call.args}")
+        print(f"Result: {result}")
+```
+
+### Understanding `dspy.Tool`
+
+The `dspy.Tool` class wraps regular Python functions to make them compatible with DSPy's tool system:
+
+```python
+def my_function(param1: str, param2: int = 5) -> str:
+    """A sample function with parameters."""
+    return f"Processed {param1} with value {param2}"
+
+# Create a tool
+tool = dspy.Tool(my_function)
+
+# Tool properties
+print(tool.name)        # "my_function"
+print(tool.desc)        # The function's docstring
+print(tool.args)        # Parameter schema
+print(str(tool))        # Full tool description
+```
+
+### Understanding `dspy.ToolCalls`
+
+The `dspy.ToolCalls` type represents the output from a model that can make tool calls:
+
+```python
+# After getting a response with tool calls
+for call in response.outputs.tool_calls:
+    print(f"Tool name: {call.name}")
+    print(f"Arguments: {call.args}")
+    
+    # Execute the tool
+    if call.name in available_tools:
+        result = available_tools[call.name](**call.args)
+        print(f"Result: {result}")
+```
+
+## Best Practices
+
+### 1. Tool Function Design
+
+- **Clear docstrings**: Tools work better with descriptive documentation
+- **Type hints**: Provide clear parameter and return types
+- **Simple parameters**: Use basic types (str, int, bool, dict, list) or Pydantic models
+
+```python
+def good_tool(city: str, units: str = "celsius") -> str:
+    """
+    Get weather information for a specific city.
+    
+    Args:
+        city: The name of the city to get weather for
+        units: Temperature units, either 'celsius' or 'fahrenheit'
+    
+    Returns:
+        A string describing the current weather conditions
+    """
+    # Implementation with proper error handling
+    if not city.strip():
+        return "Error: City name cannot be empty"
+    
+    # Weather logic here...
+    return f"Weather in {city}: 25°{units[0].upper()}, sunny"
+```
+
+### 2. Choosing Between ReAct and Manual Handling
+
+**Use `dspy.ReAct` when:**
+- You want automatic reasoning and tool selection
+- The task requires multiple tool calls
+- You need built-in error recovery
+- You want to focus on tool implementation rather than orchestration
+
+**Use manual tool handling when:**
+- You need precise control over tool execution
+- You want custom error handling logic
+- You want to minimize the latency
+- Your tool returns nothing (void function)
+
+
+Tools in DSPy provide a powerful way to extend language model capabilities beyond text generation. Whether using the fully automated ReAct approach or manual tool handling, you can build sophisticated agents that interact with the world through code.
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 55f17855c6..223e2c119c 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -18,6 +18,7 @@ nav:
             - Signatures: learn/programming/signatures.md
             - Modules: learn/programming/modules.md
             - Adapters: learn/programming/adapters.md
+            - Tools: learn/programming/tools.md
         - DSPy Evaluation:
             - Evaluation Overview: learn/evaluation/overview.md
             - Data Handling: learn/evaluation/data.md