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

# Agent Structured Output

> Get strongly-typed Pydantic models with intelligent retry logic

The MCPAgent supports structured output, allowing you to get strongly-typed Pydantic models instead of plain text responses. The agent becomes **schema-aware** and will intelligently retry to gather missing information until all required fields can be populated.

## How it Works

When you provide an `output_schema` parameter, the agent:

1. **Understands requirements** - The agent knows exactly what information it needs to collect
2. **Attempts structured output** - At completion points, tries to format the result into your schema
3. **Intelligently retries** - If required fields are missing, continues execution to gather the missing data
4. **Validates completeness** - Only finishes when all required fields can be populated

## Basic Example

<CodeGroup>
  ```python Python theme={null}
  import asyncio
  from pydantic import BaseModel, Field
  from langchain_openai import ChatOpenAI
  from mcp_use import MCPAgent, MCPClient

  class WeatherInfo(BaseModel):
      """Weather information for a location"""
      city: str = Field(description="City name")
      temperature: float = Field(description="Temperature in Celsius")
      condition: str = Field(description="Weather condition")
      humidity: int = Field(description="Humidity percentage")

  async def main():
      # Setup client and agent
      client = MCPClient(config={"mcpServers": {...}})
      llm = ChatOpenAI(model="gpt-5.5")
      agent = MCPAgent(llm=llm, client=client)

      # Get structured output
      weather: WeatherInfo = await agent.run(
          "Get the current weather in San Francisco",
          output_schema=WeatherInfo
      )

      print(f"Temperature in {weather.city}: {weather.temperature}°C")
      print(f"Condition: {weather.condition}")
      print(f"Humidity: {weather.humidity}%")

  asyncio.run(main())
  ```
</CodeGroup>

## Key Benefits

* **Type Safety**: Get Pydantic (Python) or Zod (TypeScript) models with full IDE support and validation
* **Intelligent Gathering**: Agent knows what information is required and won't stop until it has everything
* **Automatic Retry**: Missing fields trigger continued execution automatically
* **Field Validation**: Built-in validation for required fields, data types, and constraints

The agent will continue working until all required fields in your schema can be populated, ensuring you always get complete, structured data.