Codex MCP Wrapper
An MCP server that wraps the OpenAI Codex CLI, exposing its functionality through the MCP API.
Agentic Developer MCP
This project wraps OpenAI's Codex CLI as an MCP (Model Context Protocol) server, making it accessible through the TeaBranch/open-responses-server middleware.
This engine may be replaced with OpenCode or Amazon Strands
Requirements
- Node 22 (nvm install 22.15.1 | nvm use 22.15.1) required for Codex
Overview
The setup consists of three main components:
- Codex CLI: OpenAI's command-line interface for interacting with Codex.
- MCP Wrapper Server: A Node.js Express server that forwards MCP requests to Codex CLI and formats responses as MCP.
- open-responses-server: A middleware service that provides Responses API compatibility and MCP support.
Installation
Using Docker (Recommended)
# Clone this repository
git clone https://github.com/yourusername/codex-mcp-wrapper.git
cd codex-mcp-wrapper
# Start the services
./start.sh
This will start:
- Codex MCP wrapper on port 8080
- open-responses-server on port 3000
Manual Installation
# Install dependencies
npm install
# Install Codex CLI globally
npm install -g @openai/codex
# Start the MCP server
node mcp-server.js
# Install the package in development mode
pip install -e .
Usage
You can run the MCP server using either stdio or SSE transport:
# Using stdio (default)
python -m mcp_server
# Using SSE on a specific port
python -m mcp_server --transport sse --port 8000
Tool Documentation
run_codex
Clones a repository, checks out a specific branch (optional), navigates to a specific folder (optional), and runs Codex with the given request.
Parameters
- repository(required): Git repository URL
- branch(optional): Git branch to checkout
- folder(optional): Folder within the repository to focus on
- request(required): Codex request/prompt to run
Example
{
  "repository": "https://github.com/username/repo.git",
  "branch": "main",
  "folder": "src",
  "request": "Analyze this code and suggest improvements"
}
clone_and_write_prompt
Clones a repository, reads the system prompt from .agent/system.md, parses modelId from .agent/agent.json, writes the request to a .prompt file, and invokes the Codex CLI with the extracted model.
Parameters
- repository(required): Git repository URL
- request(required): Prompt text to run through Codex
- folder(optional, default- /): Subfolder within the repository to operate in
Example
{
  "repository": "https://github.com/username/repo.git",
  "folder": "src",
  "request": "Analyze this code and suggest improvements"
}
MCPS Configuration
Place a mcps.json file under the .agent/ directory to register available MCP tools. Codex will load this configuration automatically.
Example .agent/mcps.json:
{
  "mcpServers": {
    "agentic-developer-mcp": {
      "url": "..."
    }
  }
}
Development
This project uses the MCP Python SDK to implement an MCP server. The primary implementation is in mcp_server/server.py.
License
MIT
Related Servers
- Adios MCP- A remote MCP server deployable on Cloudflare Workers without authentication. 
- Tailwind Svelte Assistant- Provides documentation and code snippets for SvelteKit and Tailwind CSS. 
- Jetty.io- Work on dataset metadata with MLCommons Croissant validation and creation. 
- Interactive Feedback MCP- Provides interactive user feedback and command execution for AI-assisted development. 
- MCPizer- Enables AI assistants to call any REST API or gRPC service by automatically converting their schemas into MCP tools. 
- Remote MCP Server (Authless)- An authentication-free, remote MCP server designed for deployment on Cloudflare Workers or local setup via npm. 
- ADB Friend- A CLI tool for developers to manage Android devices via ADB. 
- 302AI Image- A Model Context Protocol server for generating images using the 302AI API. 
- SwissArmyHammer- Manage AI prompts as local markdown files. 
- Deliberate Reasoning Engine (DRE)- Transforms linear AI reasoning into structured, auditable thought graphs, enabling language models to externalize their reasoning process as a directed acyclic graph (DAG).