Multi-MCP

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README

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

<!-- mcp-name: io.github.religa/multi-mcp -->

[![CI](https://github.com/religa/multi_mcp/workflows/CI/badge.svg)](https://github.com/religa/multi_mcp/actions)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)

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.

![Demo Video](https://github.com/user-attachments/assets/39c3f100-e20d-4c3d-8130-b01c401f2d29)

## 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

<!-- Claude Code Plugin - Coming Soon
#### Option 1: Claude Code Plugin (Recommended)

```bash
# Add the marketplace
/plugin marketplace add religa/multi_mcp

# Install the plugin
/plugin install multi-mcp@multi_mcp
```

Then configure API keys in `~/.multi_mcp/.env` (see [Configuration](#configuration)).
-->

#### Option 1: From Source

```bash
# 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`:

```bash
# 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:**

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

**OpenCode:**

```json
{
  "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:

```bash
# 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)

Models are defined in YAML configuration files (user config wins):
1. **Pac