multi-mcp

Multi-MCP: Multi-Model Code Review and Analysis MCP Server for Claude Code

A multi-model AI orchestration MCP server for automated code review and LLM-powered analysis. Multi-MCP integrates with Claude Code CLI and OpenCode to orchestrate multiple AI models (OpenAI GPT, Anthropic Claude, Google Gemini) for code quality checks, security analysis (OWASP Top 10), and multi-agent consensus. Built on the Model Context Protocol (MCP), this tool enables Python developers and DevOps teams to automate code reviews with AI-powered insights directly in their development workflow.

Features

• 🔍 Code Review - Systematic workflow with OWASP Top 10 security checks and performance analysis
• 💬 Chat - Interactive development assistance with repository context awareness
• 🔄 Compare - Parallel multi-model analysis for architectural decisions
• 🎭 Debate - Multi-agent consensus workflow (independent answers + critique)
• 🤖 Multi-Model Support - OpenAI GPT, Anthropic Claude, Google Gemini, and OpenRouter
• 🖥️ CLI & API Models - Mix CLI-based (Gemini CLI, Codex CLI) and API models
• 🏷️ Model Aliases - Use short names like mini, sonnet, gemini
• 🧵 Threading - Maintain context across multi-step reviews

How It Works

Multi-MCP acts as an MCP server that Claude Code or OpenCode connects to, providing AI-powered code analysis tools:

1. Install the MCP server and configure your AI model API keys
2. Integrate with Claude Code or OpenCode automatically via make install
3. Invoke tools using natural language (e.g., "multi codereview this file")
4. Get Results from multiple AI models orchestrated in parallel

Performance

Fast Multi-Model Analysis:

• ⚡ Parallel Execution - 3 models in ~10s (vs ~30s sequential)
• 🔄 Async Architecture - Non-blocking Python asyncio
• 💾 Conversation Threading - Maintains context across multi-step reviews
• 📊 Low Latency - Response time = slowest model, not sum of all models

Quick Start

Prerequisites:

• Python 3.11+
• API key for at least one provider (OpenAI, Anthropic, Google, or OpenRouter)

Installation

Option 1: From Source

# Clone and install
git clone https://github.com/religa/multi_mcp.git
cd multi_mcp
# Execute ./scripts/install.sh
make install

# The installer will:
# 1. Install dependencies (uv sync)
# 2. Generate your .env file
# 3. Automatically add to Claude Code / OpenCode config (requires jq)
# 4. Test the installation

Option 2: Manual Configuration

If you prefer not to run make install:

# Install dependencies
uv sync

# Copy and configure .env
cp .env.example .env
# Edit .env with your API keys

Add to Claude Code (~/.claude.json) or OpenCode (~/.opencode/opencode.json), replacing /path/to/multi_mcp with your actual clone path:

Claude Code:

{
  "mcpServers": {
    "multi": {
      "type": "stdio",
      "command": "/path/to/multi_mcp/.venv/bin/python",
      "args": ["-m", "multi_mcp.server"]
    }
  }
}

OpenCode:

{
  "mcp": {
    "multi": {
      "type": "local",
      "command": ["/path/to/multi_mcp/.venv/bin/python", "-m", "multi_mcp.server"],
      "enabled": true
    }
  }
}

Configuration

Environment Configuration (API Keys & Settings)

Multi-MCP loads settings from .env files in this order (highest priority first):

1. Environment variables (already set in shell)
2. Project .env (current directory or project root)
3. User .env (~/.multi_mcp/.env) - fallback for pip installs

Edit .env with your API keys:

# API Keys (configure at least one)
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
OPENROUTER_API_KEY=sk-or-...

# Azure OpenAI (optional)
AZURE_API_KEY=...
AZURE_API_BASE=https://your-resource.openai.azure.com/

# AWS Bedrock (optional)
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
AWS_REGION_NAME=us-east-1

# Model Configuration
DEFAULT_MODEL=gpt-5-mini
DEFAULT_MODEL_LIST=gpt-5-mini,gemini-3-flash

Model Configuration (Adding Custom Models)