官方说明7 个来源MCP 服务8 天前
religa/multi-mcp
🐍 🍎 🪟 🐧 - Parallel multi-model code review, security analysis and debate with ChatGPT, Claude and Gemini,…
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官方说明🐍 🍎 🪟 🐧 - Parallel multi-model code review, security analysis and debate with ChatGPT, Claude and Gemini, including consensus and OWASP Top 10 checks.
配置模板（请按你的客户端调整）
claude mcp add multi-mcp -- npx -y multi_mcp
这是根据仓库名生成的可粘贴骨架，命令行参数与包名请以该项目 README 为准。
- ★ 数取值
- 36 ★ · religa/multi_mcp · 取自该仓库的 GitHub 页面 · （2026-10-16 20:00:20 核对）
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
- 评分构成
- 综合分 703 · 36 ★ · 7 个来源 · mcp
常见问题（答案都来自我们的记录）
怎么安装？
我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。
它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
religa/multi_mcp；我们收录于 2026-10-09；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。第三方质量标记
Multi-MCP: Multi-Model Code Review and Analysis MCP Server for Claude Code
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
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
Option 2: Manual Configuration
If you prefer not to run make install:
Add to Claude Code (~/.claude.json) or OpenCode (~/.opencode/opencode.json), replacing /path/to/multi_mcp with your actual clone path:
**Claude Code:**
**OpenCode:**
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:
Model Configuration (Adding Custom Models)
Models are defined in YAML configuration files (user config wins):
1. **Package defaults**: multi_mcp/config/config.yaml (bundled with package)
2. **User overrides**: ~/.multi_mcp/config.yaml (optional, takes precedence)
To add your own models, create ~/.multi_mcp/config.yaml (see config.yaml and config.override.example.yaml for examples):
**Merge behavior:**
- New models are added alongside package defaults
- Existing models are merged (your settings override package defaults)
- Aliases can be "stolen" from package models to your custom models
Usage Examples
Once installed in your MCP client (Claude Code or OpenCode), you can use these commands:
**💬 Chat** - Interactive development assistance:
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
Option 2: Manual Configuration
If you prefer not to run make install:
Add to Claude Code (~/.claude.json) or OpenCode (~/.opencode/opencode.json), replacing /path/to/multi_mcp with your actual clone path:
**Claude Code:**
**OpenCode:**
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:
Model Configuration (Adding Custom Models)
Models are defined in YAML configuration files (user config wins):
1. **Package defaults**: multi_mcp/config/config.yaml (bundled with package)
2. **User overrides**: ~/.multi_mcp/config.yaml (optional, takes precedence)
To add your own models, create ~/.multi_mcp/config.yaml (see config.yaml and config.override.example.yaml for examples):
**Merge behavior:**
- New models are added alongside package defaults
- Existing models are merged (your settings override package defaults)
- Aliases can be "stolen" from package models to your custom models
Usage Examples
Once installed in your MCP client (Claude Code or OpenCode), you can use these commands:
**💬 Chat** - Interactive development assistance:
- 来源
- awesome · https://github.com/religa/multi_mcp
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