by BeehiveInnovations
Use Claude Code, Gemini CLI, Codex CLI, or any MCP client with any AI model. Acts as a multi-model proxy supporting Open
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GitHub stars
Acts as a proxy that lets you use multiple AI models (OpenAI, Gemini, Claude, etc.) within a single MCP session and connect external AI CLIs together.
PAL MCP Server is a community-built MCP server published by BeehiveInnovations that provides AI assistants with tools and capabilities via the Model Context Protocol. Use Claude Code, Gemini CLI, Codex CLI, or any MCP client with any AI model. Acts as a multi-model proxy supporting Open It is categorized under ai ml, developer tools.
You can install PAL MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
NOASSERTION
PAL MCP Server is released under the NOASSERTION license.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research β Summarize β Create document β Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired PAL MCP Server into a staging workspace; the listingβs GitHub and npm pointers saved time versus hunting across READMEs.
PAL MCP Server reduced integration guesswork β categories and install configs on the listing matched the upstream repo.
PAL MCP Server is a well-scoped MCP server in the explainx.ai directory β install snippets and categories matched our Claude Code setup.
PAL MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We evaluated PAL MCP Server against two servers with overlapping tools; this profile had the clearer scope statement.
According to our notes, PAL MCP Server benefits from clear Model Context Protocol framing β fewer ambiguous βAI pluginβ claims.
I recommend PAL MCP Server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
PAL MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated PAL MCP Server against two servers with overlapping tools; this profile had the clearer scope statement.
According to our notes, PAL MCP Server benefits from clear Model Context Protocol framing β fewer ambiguous βAI pluginβ claims.
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<em>Your AI's PAL β a Provider Abstraction Layer</em><br /> <sub><a href="docs/name-change.md">Formerly known as Zen MCP</a></sub>
π Watch more examples
Use the π€ CLI you love:
Claude Code Β· Gemini CLI Β· Codex CLI Β· Qwen Code CLI Β· Cursor Β· and more
With multiple models within a single prompt:
Gemini Β· OpenAI Β· Anthropic Β· Grok Β· Azure Β· Ollama Β· OpenRouter Β· DIAL Β· On-Device Model
The new clink (CLI + Link) tool connects external AI CLIs directly into your workflow:
planner, codereviewer, or custom role agents with specialized system prompts# 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 β 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
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.
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 β automated planning β implementation β pre-commit validation.
<details> <summary><b>Reasons to Use PAL MCP</b></summary>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.
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!
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 codereviewcodereview workflow where Claude walks the code, looking for all kinds of issuesconfidence level between exploring, low, medium, high and certain to track how confidently it's been able to find and identify issuescodereviewplanner workflow to break the work down into simpler steps if a major refactor is requiredprecommit reviewAll 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.
</details>Remember: Claude stays in full control β but YOU call the shots. PAL is designed to have Claude engage other models only when needed β 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 β or fly solo. You're the guide. The prompter. The puppeteer.
You are the AI - Actually Intelligent.
For best results when using Claude Code:
For best results when using Codex CLI:
Prerequisites: Python 3.10+, Git, uv installed
1. Get API Keys (choose one or more):
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](https://docs.astral.sh/u
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
β Do
β Don't
π‘ Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
β Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
β Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.