PAL MCP: Many Workflows. One Context.
🆕 Now with CLI-to-CLI Bridge
The new clink (CLI + Link) tool connects external AI CLIs directly into your workflow:
- Connect external CLIs like Gemini CLI, Codex CLI, and Claude Code directly into your workflow
- CLI Subagents - Launch isolated CLI instances from within your current CLI! Claude Code can spawn Codex subagents, Codex can spawn Gemini CLI subagents, etc. Offload heavy tasks (code reviews, bug hunting) to fresh contexts while your main session's context window remains unpolluted. Each subagent returns only final results.
- Context Isolation - Run separate investigations without polluting your primary workspace
- Role Specialization - Spawn
planner, codereviewer, or custom role agents with specialized system prompts
- Full CLI Capabilities - Web search, file inspection, MCP tool access, latest documentation lookups
- Seamless Continuity - Sub-CLIs participate as first-class members with full conversation context between tools
# Codex spawns Codex subagent for isolated code review in fresh context
clink with codex codereviewer to audit auth module for security issues
# Subagent reviews in isolation, returns final report without cluttering your context as codex reads each file and walks the directory structure
# Consensus from different AI models → Implementation handoff with full context preservation between tools
Use consensus with gpt-5 and gemini-pro to decide: dark mode or offline support next
Continue with clink gemini - implement the recommended feature
# Gemini receives full debate context and starts coding immediately
👉 Learn more about clink
Why PAL MCP?
Why rely on one AI model when you can orchestrate them all?
A Model Context Protocol server that supercharges tools like Claude Code, Codex CLI, and IDE clients such
as Cursor or the Claude Dev VS Code extension. PAL MCP connects your favorite AI tool
to multiple AI models for enhanced code analysis, problem-solving, and collaborative development.
True AI Collaboration with Conversation Continuity
PAL supports conversation threading so your CLI can discuss ideas with multiple AI models, exchange reasoning, get second opinions, and even run collaborative debates between models to help you reach deeper insights and better solutions.
Your CLI always stays in control but gets perspectives from the best AI for each subtask. Context carries forward seamlessly across tools and models, enabling complex workflows like: code reviews with multiple models → automated planning → implementation → pre-commit validation.
You're in control. Your CLI of choice orchestrates the AI team, but you decide the workflow. Craft powerful prompts that bring in Gemini Pro, GPT 5, Flash, or local offline models exactly when needed.
Reasons to Use PAL MCP
A typical workflow with Claude Code as an example:
Multi-Model Orchestration - Claude coordinates with Gemini Pro, O3, GPT-5, and 50+ other models to get the best analysis for each task
Context Revival Magic - Even after Claude's context resets, continue conversations seamlessly by having other models "remind" Claude of the discussion
Guided Workflows - Enforces systematic investigation phases that prevent rushed analysis and ensure thorough code examination
Extended Context Windows - Break Claude's limits by delegating to Gemini (1M tokens) or O3 (200K tokens) for massive codebases
True Conversation Continuity - Full context flows across tools and models - Gemini remembers what O3 said 10 steps ago
Model-Specific Strengths - Extended thinking with Gemini Pro, blazing speed with Flash, strong reasoning with O3, privacy with local Ollama
Professional Code Reviews - Multi-pass analysis with severity levels, actionable feedback, and consensus from multiple AI experts
Smart Debugging Assistant - Systematic root cause analysis with hypothesis tracking and confidence levels
Automatic Model Selection - Claude intelligently picks the right model for each subtask (or you can specify)
Vision Capabilities - Analyze screenshots, diagrams, and visual content with vision-enabled models
Local Model Support - Run Llama, Mistral, or other models locally for complete privacy and zero API costs
Bypass MCP Token Limits - Automatically works around MCP's 25K limit for large prompts and responses
The Killer Feature: When Claude's context resets, just ask to "continue with O3" - the other model's response magically revives Claude's understanding without re-ingesting documents!
Example: Multi-Model Code Review Workflow
Perform a codereview using gemini pro and o3 and use planner to generate a detailed plan, implement the fixes and do a final precommit check by continuing from the previous codereview- This triggers a
codereview workflow where Claude walks the code, looking for all kinds of issues
- After multiple passes, collects relevant code and makes note of issues along the way
- Maintains a
confidence level between exploring, low, medium, high and certain to track how confidently it's been able to find and identify issues
- Generates a detailed list of critical -> low issues
- Shares the relevant files, findings, etc with Gemini Pro to perform a deep dive for a second
codereview
- Comes back with a response and next does the same with o3, adding to the prompt if a new discovery comes to light
- When done, Claude takes in all the feedback and combines a single list of all critical -> low issues, including good patterns in your code. The final list includes new findings or revisions in case Claude misunderstood or missed something crucial and one of the other models pointed this out
- It then uses the
planner workflow to break the work down into simpler steps if a major refactor is required
- Claude then performs the actual work of fixing highlighted issues
- When done, Claude returns to Gemini Pro for a
precommit review
All within a single conversation thread! Gemini Pro in step 11 knows what was recommended by O3 in step 7! Taking that context
and review into consideration to aid with its final pre-commit review.
Think of it as Claude Code for Claude Code. This MCP isn't magic. It's just super-glue.
Remember: Claude stays in full control — but YOU call the shots.
PAL is designed to have Claude engage other models only when needed — and to follow through with meaningful back-and-forth.
You're the one who crafts the powerful prompt that makes Claude bring in Gemini, Flash, O3 — or fly solo.
You're the guide. The prompter. The puppeteer.
You are the AI - Actually Intelligent.
Recommended AI Stack
For Claude Code Users
For best results when using Claude Code:
- Sonnet 4.5 - All agentic work and orchestration
- Gemini 3.0 Pro OR GPT-5.2 / Pro - Deep thinking, additional code reviews, debugging and validations, pre-commit analysis
For Codex Users
For best results when using Codex CLI:
- GPT-5.2 Codex Medium - All agentic work and orchestration
- Gemini 3.0 Pro OR GPT-5.2-Pro - Deep thinking, additional code reviews, debugging and validations, pre-commit analysis
Quick Start (5 minutes)
Prerequisites: Python 3.10+, Git, uv installed
1. Get API Keys (choose one or more):
- OpenRouter - Access multiple models with one API
- Gemini - Google's latest models
- OpenAI - O3, GPT-5 series
- Azure OpenAI - Enterprise deployments of GPT-4o, GPT-4.1, GPT-5 family
- X.AI - Grok models
- DIAL - Vendor-agnostic model access
- Ollama - Local models (free)
2. Install (choose one):
Option A: Clone and Automatic Setup (recommended)
git clone https://github.com/BeehiveInnovations/pal-mcp-server.git
cd pal-mcp-server
# Handles everything: setup, config, API keys from system environment.
# Auto-configures Claude Desktop, Claude Code, Gemini CLI, Codex CLI, Qwen CLI
# Enable / disable additional settings in .env
./run-server.sh
Option B: Instant Setup with uvx
// Add to ~/.claude/settings.json or .mcp.json
// Don't forget to add your API keys under env
{
"mcpServers": {
"pal": {
"command": "bash",
"args": ["-c", "for p in $(which uvx 2>/dev/null) $HOME/.local/bin/uvx /opt/homebrew/bin/uvx /usr/local/bin/uvx uvx; do [ -x \"$p\" ] && exec \"$p\" --from git+https://github.com/BeehiveInnovations/pal-mcp-server.git pal-mcp-server; done; echo 'uvx not found' >&2; exit 1"],
"env": {
"PATH": "/usr/local/bin:/usr/bin:/bin:/opt/homebrew/bin:~/.local/bin",
"GEMINI_API_KEY": "your-key-here",
"DISABLED_TOOLS": "analyze,refactor,testgen,secaudit,docgen,tracer",
"DEFAULT_MODEL": "auto"
}
}
}
}
3. Start Using!
"Use pal to analyze this code for security issues with gemini pro"
"Debug this error with o3 and then get flash to suggest optimizations"
"Plan the migration strategy with pal, get consensus from multiple models"
"clink with cli_name=\"gemini\" role=\"planner\" to draft a phased rollout plan"
👉 Complete Setup Guide with detailed installation, configuration for Gemini / Codex / Qwen, and troubleshooting
👉 Cursor & VS Code Setup for IDE integration instructions
📺 Watch tools in action to see real-world examples
Provider Configuration
PAL activates any provider that has credentials in your .env. See .env.example for deeper customization.
Note: Each tool comes with its own multi-step workflow, parameters, and descriptions that consume valuable context window space even when not in use. To optimize performance, some tools are disabled by default. See Tool Configuration below to enable them.
Collaboration & Planning (Enabled by default)
clink - Bridge requests to external AI CLIs (Gemini planner, codereviewer, etc.)chat - Brainstorm ideas, get second opinions, validate approaches. With capable models (GPT-5.2 Pro, Gemini 3.0 Pro), generates complete code / implementationthinkdeep - Extended reasoning, edge case analysis, alternative perspectivesplanner - Break down complex projects into structured, actionable plansconsensus - Get expert opinions from multiple AI models with stance steering
Code Analysis & Quality
debug - Systematic investigation and root cause analysisprecommit - Validate changes before committing, prevent regressionscodereview - Professional reviews with severity levels and actionable feedbackanalyze (disabled by default - enable) - Understand architecture, patterns, dependencies across entire codebases
Development Tools (Disabled by default - enable)
refactor - Intelligent code refactoring with decomposition focustestgen - Comprehensive test generation with edge casessecaudit - Security audits with OWASP Top 10 analysisdocgen - Generate documentation with complexity analysis
Utilities
apilookup - Forces current-year API/SDK documentation lookups in a sub-process (saves tokens within the current context window), prevents outdated training data responseschallenge - Prevent "You're absolutely right!" responses with critical analysistracer (disabled by default - enable) - Static analysis prompts for call-flow mapping
👉 Tool Configuration
Default Configuration
To optimize context window usage, only essential tools are enabled by default:
Enabled by default:
chat, thinkdeep, planner, consensus - Core collaboration toolscodereview, precommit, debug - Essential code quality toolsapilookup - Rapid API/SDK information lookupchallenge - Critical thinking utility
Disabled by default:
analyze, refactor, testgen, secaudit, docgen, tracer
To enable additional tools, remove them from the DISABLED_TOOLS list:
Option 1: Edit your .env file
# Default configuration (from .env.example)
DISABLED_TOOLS=analyze,refactor,testgen,secaudit,docgen,tracer
# To enable specific tools, remove them from the list
# Example: Enable analyze tool
DISABLED_TOOLS=refactor,testgen,secaudit,docgen,tracer
# To enable ALL tools
DISABLED_TOOLS=
Option 2: Configure in MCP settings
// In ~/.claude/settings.json or .mcp.json
{
"mcpServers": {
"pal": {
"env": {
// Tool configuration
"DISABLED_TOOLS": "refactor,testgen,secaudit,docgen,tracer",
"DEFAULT_MODEL": "pro",
"DEFAULT_THINKING_MODE_THINKDEEP": "high",
// API configuration
"GEMINI_API_KEY": "your-gemini-key",
"OPENAI_API_KEY": "your-openai-key",
"OPENROUTER_API_KEY": "your-openrouter-key",
// Logging and performance
"LOG_LEVEL": "INFO",
"CONVERSATION_TIMEOUT_HOURS": "6",
"MAX_CONVERSATION_TURNS": "50"
}
}
}
}
Option 3: Enable all tools
// Remove or empty the DISABLED_TOOLS to enable everything
{
"mcpServers": {
"pal": {
"env": {
"DISABLED_TOOLS": ""
}
}
}
}
Note:
- Essential tools (
version, listmodels) cannot be disabled
- After changing tool configuration, restart your Claude session for changes to take effect
- Each tool adds to context window usage, so only enable what you need
Chat Tool - Collaborative decision making and multi-turn conversations
Picking Redis vs Memcached:
Chat Redis or Memcached_web.webm
Multi-turn conversation with continuation:
Chat With Gemini_web.webm
Consensus Tool - Multi-model debate and decision making
Multi-model consensus debate:
PAL Consensus Debate
PreCommit Tool - Comprehensive change validation
Pre-commit validation workflow:
API Lookup Tool - Current vs outdated API documentation
Without PAL - outdated APIs:
API without PAL
With PAL - current APIs:
API with PAL
Challenge Tool - Critical thinking vs reflexive agreement
Without PAL:
With PAL:
Key Features
AI Orchestration
- Auto model selection - Claude picks the right AI for each task
- Multi-model workflows - Chain different models in single conversations
- Conversation continuity - Context preserved across tools and models
- Context revival - Continue conversations even after context resets
Model Support
- Multiple providers - Gemini, OpenAI, Azure, X.AI, OpenRouter, DIAL, Ollama
- Latest models - GPT-5, Gemini 3.0 Pro, O3, Grok-4, local Llama
- Thinking modes - Control reasoning depth vs cost
- Vision support - Analyze images, diagrams, screenshots
Developer Experience
- Guided workflows - Systematic investigation prevents rushed analysis
- Smart file handling - Auto-expand directories, manage token limits
- Web search integration - Access current documentation and best practices
- Large prompt support - Bypass MCP's 25K token limit
Example Workflows
Multi-model Code Review:
"Perform a codereview using gemini pro and o3, then use planner to create a fix strategy"
→ Claude reviews code systematically → Consults Gemini Pro → Gets O3's perspective → Creates unified action plan
Collaborative Debugging:
"Debug this race condition with max thinking mode, then validate the fix with precommit"
→ Deep investigation → Expert analysis → Solution implementation → Pre-commit validation
Architecture Planning:
"Plan our microservices migration, get consensus from pro and o3 on the approach"
→ Structured planning → Multiple expert opinions → Consensus building → Implementation roadmap
👉 Advanced Usage Guide for complex workflows, model configuration, and power-user features
Quick Links
📖 Documentation
PAL MCP（原名 Zen MCP）是一个 Model Context Protocol 服务器，可以让你的 AI 编程命令行工具（Claude Code、Gemini CLI、Codex CLI 等）在同一次对话中调度多个 AI 模型（Gemini、OpenAI、Azure、Grok、OpenRouter、Ollama、本地模型）。