# Advanced Usage Guide

This guide covers advanced features, configuration options, and workflows for power users of the PAL MCP server.

## Table of Contents

- [Model Configuration](#model-configuration)
- [Model Usage Restrictions](#model-usage-restrictions)
- [Thinking Modes](#thinking-modes)
- [Tool Parameters](#tool-parameters)
- [Context Revival: AI Memory Beyond Context Limits](#context-revival-ai-memory-beyond-context-limits)
- [Collaborative Workflows](#collaborative-workflows)
- [Working with Large Prompts](#working-with-large-prompts)
- [Vision Support](#vision-support)
- [Web Search Integration](#web-search-integration)
- [System Prompts](#system-prompts)

## Model Configuration

**For basic configuration**, see the [Configuration Guide](configuration.md) which covers API keys, model selection, and environment variables.

This section focuses on **advanced model usage patterns** for power users:

**Per-Request Model Override:**
Regardless of your default configuration, you can specify models per request:
- "Use **pro** for deep security analysis of auth.py"
- "Use **flash** to quickly format this code"
- "Use **o3** to debug this logic error"
- "Review with **o4-mini** for balanced analysis"
- "Use **gpt4.1** for comprehensive codebase analysis"

**Claude's Auto Mode Decision Matrix:**

| Model | Provider | Context | Strengths | Auto Mode Usage |
|-------|----------|---------|-----------|------------------|
| **`pro`** (Gemini 3.0 Pro) | Google | 1M tokens | Extended thinking (up to 32K tokens), deep analysis | Complex architecture, security reviews, deep debugging |
| **`flash`** (Gemini 2.5 Flash) | Google | 1M tokens | Ultra-fast responses with thinking | Quick checks, formatting, simple analysis |
| **`flash-2.0`** (Gemini 2.0 Flash) | Google | 1M tokens | Latest fast model with audio/video support | Quick analysis with multimodal input |
| **`flashlite`** (Gemini 2.0 Flash Lite) | Google | 1M tokens | Lightweight text-only model | Fast text processing without vision |
| **`o3`** | OpenAI | 200K tokens | Strong logical reasoning | Debugging logic errors, systematic analysis |
| **`o3-mini`** | OpenAI | 200K tokens | Balanced speed/quality | Moderate complexity tasks |
| **`o4-mini`** | OpenAI | 200K tokens | Latest reasoning model | Optimized for shorter contexts |
| **`gpt4.1`** | OpenAI | 1M tokens | Latest GPT-4 with extended context | Large codebase analysis, comprehensive reviews |
| **`gpt5.2`** (GPT-5.2) | OpenAI | 400K tokens | Flagship reasoning model with configurable thinking effort | Complex problems, balanced agent/coding flows |
| **`gpt5.1-codex`** (GPT-5.1 Codex) | OpenAI | 400K tokens | Agentic coding specialization (Responses API) | Advanced coding tasks, structured code generation |
| **`gpt5.1-codex-mini`** (GPT-5.1 Codex mini) | OpenAI | 400K tokens | Cost-efficient Codex variant with streaming | Balanced coding tasks, cost-conscious development |
| **`gpt5`** (GPT-5) | OpenAI | 400K tokens | Advanced model with reasoning support | Complex problems requiring advanced reasoning |
| **`gpt5-mini`** (GPT-5 Mini) | OpenAI | 400K tokens | Efficient variant with reasoning | Balanced performance and capability |
| **`gpt5-nano`** (GPT-5 Nano) | OpenAI | 400K tokens | Fastest, cheapest GPT-5 variant | Summarization and classification tasks |
| **`grok-4`** | X.AI | 256K tokens | Latest flagship Grok model with reasoning, vision | Complex analysis, reasoning tasks |
| **`grok-4.1-fast-reasoning`** | X.AI | 2M tokens | High-performance Grok 4.1 Fast Reasoning with vision | Fast responses and light reasoning |
| **`llama`** (Llama 3.2) | Custom/Local | 128K tokens | Local inference, privacy | On-device analysis, cost-free processing |
| **Any model** | OpenRouter | Varies | Access to GPT-4, Claude, Llama, etc. | User-specified or based on task requirements |

**Mix & Match Providers:** Use multiple providers simultaneously! Set both `OPENROUTER_API_KEY` and `CUSTOM_API_URL` to access 
cloud models (expensive/powerful) AND local models (free/private) in the same conversation.

**Model Capabilities:**
- **Gemini Models**: Support thinking modes (minimal to max), web search, 1M context
  - **Pro 3.0**: Deep analysis with max 32K thinking tokens
  - **Flash 2.5**: Ultra-fast with thinking support (24K thinking tokens)
  - **Flash 2.0**: Latest fast model with audio/video input (24K thinking tokens)
  - **Flash Lite 2.0**: Text-only lightweight model (no thinking support)
- **O3/O4 Models**: Excellent reasoning, systematic analysis, 200K context
- **GPT-4.1**: Extended context window (1M tokens), general capabilities
- **GPT-5.2 Series**: Latest flagship reasoning models, 400K context
  - **GPT-5.2**: Flagship model with configurable thinking effort and vision
  - **GPT-5.1 Codex**: Agentic coding specialization (Responses API, non-streaming)
  - **GPT-5.1 Codex mini**: Cost-efficient Codex variant with streaming support
- **GPT-5 Series**: Advanced reasoning models, 400K context
  - **GPT-5**: Full-featured with reasoning support and vision
  - **GPT-5 Mini**: Balanced efficiency and capability
  - **GPT-5 Nano**: Optimized for fast, low-cost tasks
- **Grok-4 / Grok-4.1-fast-reasoning**: Extended thinking support, vision capabilities (256K / 2M context)

## Model Usage Restrictions

**For complete restriction configuration**, see the [Configuration Guide](configuration.md#model-usage-restrictions).

**Advanced Restriction Strategies:**

**Cost Control Examples:**
```env
# Development: Allow experimentation
GOOGLE_ALLOWED_MODELS=flash,pro
OPENAI_ALLOWED_MODELS=o4-mini,o3-mini

# Production: Cost-optimized  
GOOGLE_ALLOWED_MODELS=flash
OPENAI_ALLOWED_MODELS=o4-mini

# High-performance: Quality over cost
GOOGLE_ALLOWED_MODELS=pro
OPENAI_ALLOWED_MODELS=o3,o4-mini
```

**Important Notes:**
- Restrictions apply to all usage including auto mode
- `OPENROUTER_ALLOWED_MODELS` only affects models defined in `conf/openrouter_models.json`
- Custom local models (from `conf/custom_models.json`) are not affected by OpenRouter restrictions

## Thinking Modes

**Claude automatically manages thinking modes based on task complexity**, but you can also manually control Gemini's reasoning depth to balance between response quality and token consumption. Each thinking mode uses a different amount of tokens, directly affecting API costs and response time.

### Thinking Modes & Token Budgets

These only apply to models that support customizing token usage for extended thinking, such as Gemini 3.0 Pro.

| Mode | Token Budget | Use Case | Cost Impact |
|------|-------------|----------|-------------|
| `minimal` | 128 tokens | Simple, straightforward tasks | Lowest cost |
| `low` | 2,048 tokens | Basic reasoning tasks | 16x more than minimal |
| `medium` | 8,192 tokens | **Default** - Most development tasks | 64x more than minimal |
| `high` | 16,384 tokens | Complex problems requiring thorough analysis (default for `thinkdeep`) | 128x more than minimal |
| `max` | 32,768 tokens | Exhaustive reasoning | 256x more than minimal |

### How to Use Thinking Modes

**Claude automatically selects appropriate thinking modes**, but you can override this by explicitly requesting a specific mode in your prompts. Remember: higher thinking modes = more tokens = higher cost but better quality:

#### Optimizing Token Usage & Costs

**In most cases, let Claude automatically manage thinking modes** for optimal balance of cost and quality. Override manually when you have specific requirements:

**Use lower modes (`minimal`, `low`) to save tokens when:**
- Doing simple formatting or style checks
- Getting quick explanations of basic concepts
- Working with straightforward code
- You need faster responses
- Working within tight token budgets

**Use higher modes (`high`, `max`) when quality justifies the cost:**
- Debugging complex issues (worth the extra tokens to find root causes)
- Reviewing security-critical code (cost of tokens < cost of vulnerabilities)
- Analyzing system architecture (comprehensive analysis saves development time)
- Finding subtle bugs or edge cases
- Working on performance optimizations

**Token Cost Examples:**
- `minimal` (128 tokens) vs `max` (32,768 tokens) = 256x difference in thinking tokens
- For a simple formatting check, using `minimal` instead of the default `medium` saves ~8,000 thinking tokens
- For critical security reviews, the extra tokens in `high` or `max` mode are a worthwhile investment

**Examples by scenario:**
```
# Quick style check with o3
"Use flash to review formatting in utils.py"

# Security audit with o3
"Get o3 to do a security review of auth/ with thinking mode high"

# Complex debugging, letting claude pick the best model
"Use pal to debug this race condition with max thinking mode"

# Architecture analysis with Gemini 3.0 Pro
"Analyze the entire src/ directory architecture with high thinking using pro"
```

## Tool Parameters

All tools that work with files support **both individual files and entire directories**. The server automatically expands directories, filters for relevant code files, and manages token limits.

### File-Processing Tools

**`analyze`** - Analyze files or directories
- `files`: List of file paths or directories (required)
- `question`: What to analyze (required)  
- `model`: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.2|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
- `analysis_type`: architecture|performance|security|quality|general
- `output_format`: summary|detailed|actionable
- `thinking_mode`: minimal|low|medium|high|max (default: medium, Gemini only)
- **Web search capability**: The assistant now automatically requests web searches when it needs current documentation or best practices—no parameter required

```
"Analyze the src/ directory for architectural patterns" (auto mode picks best model)
"Use flash to quickly analyze main.py and tests/ to understand test coverage" 
"Use o3 for logical analysis of the algorithm in backend/core.py"
"Use pro for deep analysis of the entire backend/ directory structure"
```

**`codereview`** - Review code files or directories
- `files`: List of file paths or directories (required)
- `model`: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.2|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
- `review_type`: full|security|performance|quick
- `focus_on`: Specific aspects to focus on
- `standards`: Coding standards to enforce
- `severity_filter`: critical|high|medium|all
- `thinking_mode`: minimal|low|medium|high|max (default: medium, Gemini only)

```
"Review the entire api/ directory for security issues" (auto mode picks best model)
"Use pro to review auth/ for deep security analysis"
"Use o3 to review logic in algorithms/ for correctness"
"Use flash to quickly review src/ with focus on performance, only show critical issues"
```

**`debug`** - Debug with file context
- `error_description`: Description of the issue (required)
- `model`: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.2|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
- `error_context`: Stack trace or logs
- `files`: Files or directories related to the issue
- `runtime_info`: Environment details
- `previous_attempts`: What you've tried
- `thinking_mode`: minimal|low|medium|high|max (default: medium, Gemini only)
- **Web search capability**: Automatically initiates searches for relevant error messages or recent fixes when needed

```
"Debug this logic error with context from backend/" (auto mode picks best model)
"Use o3 to debug this algorithm correctness issue"
"Use pro to debug this complex architecture problem"
```

**`thinkdeep`** - Extended analysis with file context
- `current_analysis`: Your current thinking (required)
- `model`: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.2|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
- `problem_context`: Additional context
- `focus_areas`: Specific aspects to focus on
- `files`: Files or directories for context
- `thinking_mode`: minimal|low|medium|high|max (default: max, Gemini only)
- **Web search capability**: Automatically calls for research when architecture references or external insights are required

```
"Think deeper about my design with reference to src/models/" (auto mode picks best model)
"Use pro to think deeper about this architecture with extended thinking"
"Use o3 to think deeper about the logical flow in this algorithm"
```

**`testgen`** - Comprehensive test generation with edge case coverage
- `files`: Code files or directories to generate tests for (required)
- `prompt`: Description of what to test, testing objectives, and scope (required)
- `model`: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.2|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
- `test_examples`: Optional existing test files as style/pattern reference
- `thinking_mode`: minimal|low|medium|high|max (default: medium, Gemini only)

```
"Generate tests for User.login() method with edge cases" (auto mode picks best model)
"Use pro to generate comprehensive tests for src/payment.py with max thinking mode"
"Use o3 to generate tests for algorithm correctness in sort_functions.py"
"Generate tests following patterns from tests/unit/ for new auth module"
```

**`refactor`** - Intelligent code refactoring with decomposition focus
- `files`: Code files or directories to analyze for refactoring opportunities (required)
- `prompt`: Description of refactoring goals, context, and specific areas of focus (required)
- `refactor_type`: codesmells|decompose|modernize|organization (required)
- `model`: auto|pro|flash|flash-2.0|flashlite|o3|o3-mini|o4-mini|gpt4.1|gpt5.2|gpt5.1-codex|gpt5.1-codex-mini|gpt5|gpt5-mini|gpt5-nano (default: server default)
- `focus_areas`: Specific areas to focus on (e.g., 'performance', 'readability', 'maintainability', 'security')
- `style_guide_examples`: Optional existing code files to use as style/pattern reference
- `thinking_mode`: minimal|low|medium|high|max (default: medium, Gemini only)
- `continuation_id`: Thread continuation ID for multi-turn conversations

```
"Analyze legacy codebase for decomposition opportunities" (auto mode picks best model)
"Use pro to identify code smells in the authentication module with max thinking mode"
"Use pro to modernize this JavaScript code following examples/modern-patterns.js"
"Refactor src/ for better organization, focus on maintainability and readability"
```

## Context Revival: AI Memory Beyond Context Limits

**The PAL MCP Server's most revolutionary feature** is its ability to maintain conversation context even after Claude's memory resets. This enables truly persistent AI collaboration across multiple sessions and context boundaries.

### **The Breakthrough**

Even when Claude's context resets or compacts, conversations can continue seamlessly because other models (O3, Gemini) have access to the complete conversation history stored in memory and can "remind" Claude of everything that was discussed.

### Key Benefits

- **Persistent conversations** across Claude's context resets
- **Cross-tool continuation** with full context preservation
- **Multi-session workflows** that maintain complete history
- **True AI orchestration** where models can build on each other's work
- **Seamless handoffs** between different tools and models

### Quick Example

```
Session 1: "Design a RAG system with gemini pro"
[Claude's context resets]
Session 2: "Continue our RAG discussion with o3"
→ O3 receives the full history and reminds Claude of everything discussed
```

**📖 [Read the complete Context Revival guide](context-revival.md)** for detailed examples, technical architecture, configuration options, and best practices.

**See also:** [AI-to-AI Collaboration Guide](ai-collaboration.md) for multi-model coordination and conversation threading.

## Collaborative Workflows

### Design → Review → Implement
```
Think hard about designing and developing a fun calculator app in swift. Review your design plans with o3, taking in
their suggestions but keep the feature-set realistic and doable without adding bloat. Begin implementing and in between
implementation, get a codereview done by Gemini Pro and chat with Flash if you need to for creative directions.   
```

### Code → Review → Fix
```
Implement a new screen where the locations taken from the database display on a map, with pins falling from
the top and landing with animation. Once done, codereview with gemini pro and o3 both and ask them to critique your
work. Fix medium to critical bugs / concerns / issues and show me the final product
```

### Debug → Analyze → Solution → Precommit Check → Publish
```
Take a look at these log files saved under subfolder/diagnostics.log there's a bug where the user says the app
crashes at launch. Think hard and go over each line, tallying it with corresponding code within the project. After
you've performed initial investigation, ask gemini pro to analyze the log files and the related code where you 
suspect lies the bug and then formulate and implement a bare minimal fix. Must not regress. Perform a precommit
with pal in the end using gemini pro to confirm we're okay to publish the fix 
```

### Refactor → Review → Implement → Test
```
Use pal to analyze this legacy authentication module for decomposition opportunities. The code is getting hard to 
maintain and we need to break it down. Use gemini pro with high thinking mode to identify code smells and suggest 
a modernization strategy. After reviewing the refactoring plan, implement the changes step by step and then 
generate comprehensive tests with pal to ensure nothing breaks.
```

### Tool Selection Guidance
To help choose the right tool for your needs:

**Decision Flow:**
1. **Have a specific error/exception?** → Use `debug`
2. **Want to find bugs/issues in code?** → Use `codereview`
3. **Want to understand how code works?** → Use `analyze`
4. **Need comprehensive test coverage?** → Use `testgen`
5. **Want to refactor/modernize code?** → Use `refactor`
6. **Have analysis that needs extension/validation?** → Use `thinkdeep`
7. **Want to brainstorm or discuss?** → Use `chat`

**Key Distinctions:**
- `analyze` vs `codereview`: analyze explains, codereview prescribes fixes
- `chat` vs `thinkdeep`: chat is open-ended, thinkdeep extends specific analysis
- `debug` vs `codereview`: debug diagnoses runtime errors, review finds static issues
- `testgen` vs `debug`: testgen creates test suites, debug just finds issues and recommends solutions
- `refactor` vs `codereview`: refactor suggests structural improvements, codereview finds bugs/issues
- `refactor` vs `analyze`: refactor provides actionable refactoring steps, analyze provides understanding

## Vision Support

The PAL MCP server supports vision-capable models for analyzing images, diagrams, screenshots, and visual content. Vision support works seamlessly with all tools and conversation threading.

**Supported Models:**
- **Gemini 3.0 Pro & Flash**: Excellent for diagrams, architecture analysis, UI mockups (up to 20MB total)
- **OpenAI O3/O4 series**: Strong for visual debugging, error screenshots (up to 20MB total)
- **Claude models via OpenRouter**: Good for code screenshots, visual analysis (up to 5MB total)
- **Custom models**: Support varies by model, with 40MB maximum enforced for abuse prevention

**Usage Examples:**
```bash
# Debug with error screenshots
"Use pal to debug this error with the stack trace screenshot and error.py"

# Architecture analysis with diagrams  
"Analyze this system architecture diagram with gemini pro for bottlenecks"

# UI review with mockups
"Chat with flash about this UI mockup - is the layout intuitive?"

# Code review with visual context
"Review this authentication code along with the error dialog screenshot"
```

**Image Formats Supported:**
- **Images**: JPG, PNG, GIF, WebP, BMP, SVG, TIFF
- **Documents**: PDF (where supported by model)
- **Data URLs**: Base64-encoded images from Claude

**Key Features:**
- **Automatic validation**: File type, magic bytes, and size validation
- **Conversation context**: Images persist across tool switches and continuation
- **Budget management**: Automatic dropping of old images when limits exceeded
- **Model capability-aware**: Only sends images to vision-capable models

**Best Practices:**
- Describe images when including them: "screenshot of login error", "system architecture diagram"
- Use appropriate models: Gemini for complex diagrams, O3 for debugging visuals
- Consider image sizes: Larger images consume more of the model's capacity

## Working with Large Prompts

The MCP protocol has a combined request+response limit of approximately 25K tokens. This server intelligently works around this limitation by automatically handling large prompts as files:

**How it works:**
1. When you send a prompt larger than the configured limit (default: 50K characters ~10-12K tokens), the server detects this
2. It responds with a special status asking Claude to save the prompt to a file named `prompt.txt`
3. Claude saves the prompt and resends the request with the file path instead
4. The server reads the file content directly into Gemini's 1M token context
5. The full MCP token capacity is preserved for the response

**Example scenario:**
```
# You have a massive code review request with detailed context
User: "Use gemini to review this code: [50,000+ character detailed analysis]"

# Server detects the large prompt and responds:
PAL MCP: "The prompt is too large for MCP's token limits (>50,000 characters). 
Please save the prompt text to a temporary file named 'prompt.txt' and resend 
the request with an empty prompt string and the absolute file path included 
in the absolute_file_paths parameter, along with any other files you wish to share as context."

# Claude automatically handles this:
- Saves your prompt to /tmp/prompt.txt
- Resends: "Use gemini to review this code" with absolute_file_paths=["/tmp/prompt.txt", "/path/to/code.py"]

# Server processes the large prompt through Gemini's 1M context
# Returns comprehensive analysis within MCP's response limits
```

This feature ensures you can send arbitrarily large prompts to Gemini without hitting MCP's protocol limitations, while maximizing the available space for detailed responses.

## Web Search Integration

**Smart web search recommendations for enhanced analysis**

Web search is now enabled by default for all tools. Instead of performing searches directly, Gemini intelligently analyzes when additional information from the web would enhance its response and provides specific search recommendations for Claude to execute.

**How it works:**
1. Gemini analyzes the request and identifies areas where current documentation, API references, or community solutions would be valuable
2. It provides its analysis based on its training data
3. If web searches would strengthen the analysis, Gemini includes a "Recommended Web Searches for Claude" section
4. Claude can then perform these searches and incorporate the findings

**Example:**
```
User: "Use gemini to debug this FastAPI async error"

Gemini's Response:
[... debugging analysis ...]

**Recommended Web Searches for Claude:**
- "FastAPI async def vs def performance 2024" - to verify current best practices for async endpoints
- "FastAPI BackgroundTasks memory leak" - to check for known issues with the version you're using
- "FastAPI lifespan context manager pattern" - to explore proper resource management patterns

Claude can then search for these specific topics and provide you with the most current information.
```

**Benefits:**
- Always access to latest documentation and best practices
- Gemini focuses on reasoning about what information would help
- Claude maintains control over actual web searches
- More collaborative approach between the two AI assistants
- Reduces hallucination by encouraging verification of assumptions

**Web search control:**
Web search is enabled by default, allowing models to request Claude perform searches for current documentation and solutions. If you prefer the model to work only with its training data, you can disable web search:
```
"Use gemini to review this code and confirm whether any new framework changes affect the recommendation"
```

## System Prompts

The server uses carefully crafted system prompts to give each tool specialized expertise:

### Prompt Architecture
- **Centralized Prompts**: Each tool's system prompt lives in `systemprompts/` (for example, `systemprompts/chat_prompt.py`)
- **Tool Integration**: Each tool inherits from `BaseTool` and implements `get_system_prompt()`
- **Prompt Flow**: `User Request → Tool Selection → System Prompt + Context → Model Response`

### Specialized Expertise
Each tool has a unique system prompt that defines its role and approach:
- **`thinkdeep`**: Acts as a senior development partner, challenging assumptions and finding edge cases
- **`codereview`**: Expert code reviewer with security/performance focus, uses severity levels
- **`debug`**: 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 the prompt file in `systemprompts/` (and export it via `systemprompts/__init__.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)
