my-pal-mcp-server/docs/tools/chat.md

Chat Tool - General Development Chat & Collaborative Thinking

Your thinking partner - bounce ideas, get second opinions, brainstorm collaboratively

The chat tool is your collaborative thinking partner for development conversations. It's designed to help you brainstorm, validate ideas, get second opinions, and explore alternatives in a conversational format.

Thinking Mode

Default is medium (8,192 tokens). Use low for quick questions to save tokens, or high for complex discussions when thoroughness matters.

Example Prompt

I need to pick between Redis and Memcached for session storage and I need an expert opinion for the project
I'm working on. Take a look at the code and get an idea of what this project does, pick one of the two options
and then chat with gemini pro and continue discussing pros and cons to come to a final conclusion. I need a one
word verdict in the end.

Chat Redis or Memcached_web.webm

Another Example:

• We ask Claude code to pick one of two frameworks, then chat with gemini to make a final decision
• Gemini responds, confirming choice. We use continuation to ask another question using the same conversation thread
• Gemini responds with explanation. We use continuation again, using /pal:continue (MCP) command the second time

Chat With Gemini_web.webm

Key Features

• Collaborative thinking partner for your analysis and planning
• Get second opinions on your designs and approaches
• Brainstorm solutions and explore alternatives together
• Structured code generation: When using GPT-5.1 or Gemini 3.0 / 2.5 Pro, get complete, production-ready implementations saved to pal_generated.code for your CLI to review and apply
• Validate your checklists and implementation plans
• General development questions and explanations
• Technology comparisons and best practices
• Architecture and design discussions
• File reference support: "Use gemini to explain this algorithm with context from algorithm.py"
• Image support: Include screenshots, diagrams, UI mockups for visual analysis: "Chat with gemini about this error dialog screenshot to understand the user experience issue"
• Dynamic collaboration: Models can request additional files or context during the conversation if needed for a more thorough response
• Web search awareness: Automatically identifies when online research would help and instructs Claude to perform targeted searches using continuation IDs

Tool Parameters

• prompt: Your question or discussion topic (required)
• model: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.1|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
• absolute_file_paths: Optional absolute file or directory paths for additional context
• images: Optional images for visual context (absolute paths)
• working_directory_absolute_path: Required - Absolute path to an existing directory where generated code artifacts will be saved
• temperature: Response creativity (0-1, default 0.5)
• thinking_mode: minimal|low|medium|high|max (default: medium, Gemini only)
• continuation_id: Continue previous conversations

Structured Code Generation

When using advanced reasoning models like GPT-5 Pro or Gemini 3.0 Pro, the chat tool can generate complete, production-ready code implementations in a structured format.

How It Works

1. You ask your AI agent to implement a complex new feature using chat with a higher-reasoning model such as GPT-5 Pro or Gemini 3.0 Pro
2. The model generates structured implementation and shares the complete implementation with PAL
3. PAL saves the code to pal_generated.code and asks AI agent to implement the plan
4. AI agent continues from the previous context, reads the file, applies the implementation

When Code Generation Activates

The structured format activates for substantial implementation work:

• Creating new features from scratch with multiple files or significant code
• Major refactoring across multiple files or large sections
• Implementing new modules, components, or subsystems
• Large-scale updates affecting substantial portions of the codebase
• Complete rewrites of functions, algorithms, or approaches

For minor changes (small tweaks, bug fixes, algorithm improvements), the model responds normally with inline code blocks.

Example Usage

chat with gpt-5-pro and ask it to make me a standalone, classic version of the
Pacman game using pygame that I can run from the commandline. Give me a single
script to execute in the end with any / all dependencies setup for me. 
Do everything using pygame, we have no external resources / images / audio at
hand. Instead of ghosts, it'll be different geometric shapes moving around 
in the maze that Pacman can eat (so there are no baddies). Pacman gets to eat
everything including bread-crumbs and large geometric shapes but make me the
classic maze / walls that it navigates within using keyboard arrow keys.

See the Configuration Guide for details on the allow_code_generation flag.

Usage Examples

Basic Development Chat:

"Chat with pal about the best approach for user authentication in my React app"

Technology Comparison:

"Use flash to discuss whether PostgreSQL or MongoDB would be better for my e-commerce platform"

Architecture Discussion:

"Chat with pro about microservices vs monolith architecture for my project, consider scalability and team size"

File Context Analysis:

"Use gemini to chat about the current authentication implementation in auth.py and suggest improvements"

Visual Analysis:

"Chat with gemini about this UI mockup screenshot - is the user flow intuitive?"

Best Practices

• Be specific about context: Include relevant files or describe your project scope
• Ask for trade-offs: Request pros/cons for better decision-making
• Use conversation continuation: Build on previous discussions with continuation_id
• Leverage visual context: Include diagrams, mockups, or screenshots when discussing UI/UX
• Encourage research: When you suspect documentation has changed, explicitly ask the assistant to confirm by requesting a web search

When to Use Chat vs Other Tools

• Use chat for: Open-ended discussions, brainstorming, getting second opinions, technology comparisons
• Use thinkdeep for: Extending specific analysis, challenging assumptions, deeper reasoning
• Use analyze for: Understanding existing code structure and patterns
• Use debug for: Specific error diagnosis and troubleshooting