> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/MicrosoftDocs/azure-ai-docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Function Calling Tool

> Define custom functions for agents to extend capabilities with business logic, API calls, and database queries.

# Function Calling

Function calling enables agents to invoke custom functions you define, extending their capabilities with business logic, external APIs, and system integrations.

## How It Works

1. **Define functions** with clear descriptions and parameters
2. **Agent determines need** based on user request
3. **Run status changes** to `requires_action`
4. **Your code executes** the actual function
5. **Submit results** back to agent
6. **Agent processes** and generates response

<Warning>
  The agent requests function calls but doesn't execute them. Your application code must handle execution.
</Warning>

## Quick Start

<CodeGroup>
  ```python Python theme={null}
  from azure.ai.projects.models import FunctionTool
  import json

  def get_weather(location: str) -> str:
      """
      Get current weather for a location.
      
      :param location: City and state (e.g., "Seattle, WA")
      :return: Weather as JSON string
      """
      # Mock data
      weather_data = {
          "Seattle, WA": "Sunny, 72°F",
          "New York, NY": "Cloudy, 65°F"
      }
      return json.dumps({"weather": weather_data.get(location, "Unknown")})

  # Register function
  function_tool = FunctionTool(functions={get_weather})

  agent = project.agents.create_agent(
      model="gpt-4o",
      name="weather-assistant",
      instructions="Help users check weather using the get_weather function",
      tools=function_tool.definitions,
  )
  ```
</CodeGroup>

## Handling Function Calls

<CodeGroup>
  ```python Python theme={null}
  run = project.agents.runs.create(thread_id=thread.id, agent_id=agent.id)

  while run.status in ["queued", "in_progress", "requires_action"]:
      if run.status == "requires_action":
          tool_outputs = []
          for tool_call in run.required_action.submit_tool_outputs.tool_calls:
              if tool_call.function.name == "get_weather":
                  args = json.loads(tool_call.function.arguments)
                  output = get_weather(args["location"])
                  tool_outputs.append({
                      "tool_call_id": tool_call.id,
                      "output": output
                  })
          
          project.agents.runs.submit_tool_outputs(
              thread_id=thread.id,
              run_id=run.id,
              tool_outputs=tool_outputs
          )
      
      run = project.agents.runs.get(thread_id=thread.id, run_id=run.id)
  ```
</CodeGroup>

## Best Practices

* Write clear function descriptions
* Define required parameters explicitly
* Return structured JSON
* Handle errors gracefully
* Implement timeout logic (runs expire after 10 minutes)

See [Azure Functions](/foundry/agents/tools/azure-functions) for serverless hosting.