refactor: remove MAX_OUTPUT_TOKENS configuration

Remove the hardcoded 32,768 token output limit to allow Gemini to use
its default/dynamic output token allocation. This provides more
flexibility for responses without artificial constraints.

- Remove MAX_OUTPUT_TOKENS constant from config
- Remove max_tokens parameter from ToolRequest base model
- Clean up all references in server.py and tools/base.py
- Remove test_output_tokens.py as it's no longer needed
- Update imports to remove MAX_OUTPUT_TOKENS references

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

tools/base.py

@@ -9,18 +9,12 @@ import google.generativeai as genai
 from mcp.types import TextContent
 from pydantic import BaseModel, Field
 
-from config import MAX_OUTPUT_TOKENS
-
-
 class ToolRequest(BaseModel):
     """Base request model for all tools"""
 
     model: Optional[str] = Field(
         None, description="Model to use (defaults to Gemini 2.5 Pro)"
     )
-    max_tokens: Optional[int] = Field(
-        MAX_OUTPUT_TOKENS, description="Maximum number of tokens in response"
-    )
     temperature: Optional[float] = Field(
         None, description="Temperature for response (tool-specific defaults)"
     )
@@ -80,10 +74,9 @@ class BaseTool(ABC):
             temperature = getattr(request, "temperature", None)
             if temperature is None:
                 temperature = self.get_default_temperature()
-            max_tokens = getattr(request, "max_tokens", MAX_OUTPUT_TOKENS)
 
             # Create and configure model
-            model = self.create_model(model_name, temperature, max_tokens)
+            model = self.create_model(model_name, temperature)
 
             # Generate response
             response = model.generate_content(prompt)
@@ -118,14 +111,13 @@ class BaseTool(ABC):
         return response
 
     def create_model(
-        self, model_name: str, temperature: float, max_tokens: int
+        self, model_name: str, temperature: float
     ) -> genai.GenerativeModel:
         """Create a configured Gemini model"""
         return genai.GenerativeModel(
             model_name=model_name,
             generation_config={
                 "temperature": temperature,
-                "max_output_tokens": max_tokens,
                 "candidate_count": 1,
             },
         )