Implement Market Researcher Agent and Tool Use

Implement the 'Market Researcher' agent using Pydantic AI. This agent should be capable of performing simulated web searches or API calls to gather market trend data. Define a Pydantic model for its output that captures key trends and their potential impact. Integrate a basic 'search' tool (you can simulate this with a function that returns predefined JSON data) and ensure the agent uses Gemini 2.5 Pro to synthesize findings into the structured Pydantic output. 

```python
from pydantic import BaseModel, Field
from pydantic_ai import Agent

# Define structured output model for market trends
class MarketTrend(BaseModel):
    name: str = Field(..., description="Name of the market trend")
    description: str = Field(..., description="Detailed description of the trend")
    impact: str = Field(..., description="Impact on SaaS industry")

class MarketResearchReport(BaseModel):
    trends: list[MarketTrend]
    summary: str

# Implement a simulated search tool
def simulated_search_tool(query: str) -> dict:
    # In a real scenario, this would call a search API
    if "agentic AI trends" in query:
        return {"data": "Recent reports show 30% YoY growth in agentic AI adoption for enterprise SaaS, driven by automation and predictive analytics. Major players are investing heavily."}
    return {"data": "No relevant data found for query."}

# Define your Market Researcher agent
class MarketResearcher(Agent):
    # ... your agent implementation using Gemini 2.5 Pro to process search results ...
    pass

# Example usage:
# researcher = MarketResearcher(model_name="gemini-2.5-pro", tools=[simulated_search_tool])
# report = researcher.run(task="Analyze current agentic AI market trends")
```