fix: increase output token limit to prevent response truncation

- Add MAX_OUTPUT_TOKENS constant set to 32,768 (Gemini 2.5 Pro's limit)
- Update all tools and chat handler to use MAX_OUTPUT_TOKENS
- Add comprehensive tests for output token configuration
- Update README with configuration details and system prompt docs

This fixes the issue where Gemini responses were being cut off at 8192 tokens,
causing Claude to repeatedly ask for the same analysis.

Fixes #1

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

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

README.md

@@ -262,6 +262,30 @@ Tools can reference files for additional context:
 "Get gemini to think deeper about my design, reference the current architecture.md"
 ```
 
+## Configuration
+
+The server includes several configurable properties that control its behavior:
+
+### Model Configuration
+- **`DEFAULT_MODEL`**: `"gemini-2.5-pro-preview-06-05"` - The default Gemini model used
+- **`MAX_CONTEXT_TOKENS`**: `1,000,000` - Maximum input context (1M tokens for Gemini 2.5 Pro)
+- **`MAX_OUTPUT_TOKENS`**: `32,768` - Maximum output tokens per response
+
+### Temperature Defaults
+Different tools use optimized temperature settings:
+- **`TEMPERATURE_ANALYTICAL`**: `0.2` - Used for code review and debugging (focused, deterministic)
+- **`TEMPERATURE_BALANCED`**: `0.5` - Used for general chat (balanced creativity/accuracy)
+- **`TEMPERATURE_CREATIVE`**: `0.7` - Used for deep thinking and architecture (more creative)
+
+### Customizing Output Length
+Each tool accepts an optional `max_tokens` parameter to override the default:
+```
+"Use gemini to analyze main.py with max_tokens 16000"
+"Get gemini to think deeper about this design with max_tokens 50000"
+```
+
+Note: The maximum supported output is 32,768 tokens for Gemini 2.5 Pro.
+
 ## Installation
 
 1. Clone the repository:
@@ -286,14 +310,37 @@ Tools can reference files for additional context:
    export GEMINI_API_KEY="your-api-key-here"
    ```
 
+## How System Prompts Work
+
+The server uses carefully crafted system prompts to give each tool specialized expertise:
+
+### Prompt Architecture
+- **Centralized Prompts**: All system prompts are defined in `prompts/tool_prompts.py`
+- **Tool Integration**: Each tool inherits from `BaseTool` and implements `get_system_prompt()`
+- **Prompt Flow**: `User Request → Tool Selection → System Prompt + Context → Gemini Response`
+
+### Specialized Expertise
+Each tool has a unique system prompt that defines its role and approach:
+- **`think_deeper`**: Acts as a senior development partner, challenging assumptions and finding edge cases
+- **`review_code`**: Expert code reviewer with security/performance focus, uses severity levels
+- **`debug_issue`**: Systematic debugger providing root cause analysis and prevention strategies
+- **`analyze`**: Code analyst focusing on architecture, patterns, and actionable insights
+
+### Customization
+To modify tool behavior, you can:
+1. Edit prompts in `prompts/tool_prompts.py` for global changes
+2. Override `get_system_prompt()` in a tool class for tool-specific changes
+3. Use the `temperature` parameter to adjust response style (0.2 for focused, 0.7 for creative)
+
 ## Contributing
 
 We welcome contributions! The modular architecture makes it easy to add new tools:
 
 1. Create a new tool in `tools/`
 2. Inherit from `BaseTool`
-3. Implement required methods
-4. Add to `TOOLS` in `server.py`
+3. Implement required methods (including `get_system_prompt()`)
+4. Add your system prompt to `prompts/tool_prompts.py`
+5. Register your tool in `TOOLS` dict in `server.py`
 
 See existing tools for examples.