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

# Input Parameters

> Pass input parameters to agents and validate the output response

Parameters in `params` become part of the agent's input, and you can only validate the resulting output response.

## Example

This example demonstrates how to pass input parameters to the agent and validate the output. The `premium_user` parameter is passed via `params` and becomes part of the agent's input, which can be accessed in the system prompt function.

### Eval Configuration

```yaml evals.yaml theme={"dark"}
- name: eval_input_has_points
  description: Pass premium_user parameter and validate output contains points information
  runnable: agent.py::agent
  params:
    prompt: "Hello, I'm a premium user"
    premium_user: True
  output:
    contains!: "10"
```

### Agent Implementation

```python agent.py theme={"dark"}
from timbal import Agent
from timbal.state import get_run_context


def get_system_prompt() -> str:
    """Build system prompt with premium_user status from input."""
    span = get_run_context().current_span()
    premium_user = span.input.get("premium_user", False)
    
    return f"""You are a points system assistant.
Premium users have 10 points.
Premium user: {premium_user}"""

agent = Agent(
    name="points_agent",
    model="openai/gpt-4.1-mini",
    system_prompt=get_system_prompt
)
```

### Running Evaluations

```bash theme={"dark"}
python -m timbal.evals.cli evals.yaml
```

## How It Works

1. **Parameters**: The `premium_user: True` parameter is passed via `params` and becomes part of the agent's input.

2. **System Prompt**: The `get_system_prompt()` function accesses the `premium_user` value from the input using `get_run_context()` and builds the system prompt dynamically.

3. **Output Validation**: The output is validated to ensure it contains "10" (the points for premium users).

## Evaluation Results

The CLI displays pytest-style output with pass/fail status:

```
──────────────────── Timbal Evals ────────────────────
collected 1 evals from 1 file

 PASSED  evals.yaml::eval_input_has_points [0.45s]
└── points_agent
    └── ✓ output.contains! ("10")

============================= 1 passed in 0.45s ==============================
```

When validation fails, the CLI shows detailed error information:

```
──────────────────── Timbal Evals ────────────────────
collected 1 evals from 1 file

 FAILED  evals.yaml::eval_input_has_points [0.48s]
└── points_agent
    └── ✗ output.contains! ("10")
        Expected: "10"
        Actual: "You currently have 0 points..."

!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1 failed in 1.94s !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
```

## Key Features

* **Input Parameters**: Pass parameters directly to the agent via `params` - these become part of the agent's input
* **Dynamic System Prompts**: Use callable functions for `system_prompt` to access input parameters and build prompts dynamically
* **Output Validation**: Validate agent responses using output validators - input parameters cannot be validated directly