An MCP server that scales development into controllable agentic, recursive flows, and build a feature from bottom-up
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"agentic-developer-mcp": {
"args": [
"-y",
"@openai/codex"
],
"command": "npx"
}
}
}Are you the author?
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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
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OpenAI Codex CLI enables code execution through malicious MCP (Model Context Protocol) configuration files
A vulnerability was identified in OpenAI Codex CLI v0.23.0 and before that enables code execution through malicious MCP (Model Context Protocol) configuration files. The attack is triggered when a user runs the codex command inside a malicious or compromised repository. Codex automatically loads project-local .env and .codex/config.toml files without requiring user confirmation, allowing attackers to embed arbitrary commands that execute immediately.
>= 0source →Codex has sandbox bypass due to bug in path configuration logic
Due to a bug in the sandbox configuration logic, Codex CLI could treat a model-generated `cwd` as the sandbox’s writable root, including paths outside of the folder where the user started their session. This logic bypassed the intended workspace boundary and enables arbitrary file writes and command execution where the Codex process has permissions - this did not impact the network-disabled sandbox restriction. **Remediation** We released a patch in Codex CLI **0.39.0** that canonicalizes and
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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
nvm install 22.15.1 | nvm use 22.15.1) required for CodexThe setup consists of three main components:
# 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:
# 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 .
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
Clones a repository, checks out a specific branch (optional), navigates to a specific folder (optional), and runs Codex with the given request.
repository (required): Git repository URLbranch (optional): Git branch to checkoutfolder (optional): Folder within the repository to focus onrequest (required): Codex request/prompt to run{
"repository": "https://github.com/username/repo.git",
"branch": "main",
"folder": "src",
"request": "Analyze this code and suggest improvements"
}
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.
repository (required): Git repository URLrequest (required): Prompt text to run through Codexfolder (optional, default /): Subfolder within the repository to operate in{
"repository": "https://github.com/username/repo.git",
"folder": "src",
"request": "Analyze this code and suggest improvements"
}
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": "..."
}
}
}
This project uses the MCP Python SDK to implement an MCP server. The primary implementation is in mcp_server/server.py.
MIT