What problem does MCP-Codex solve?
Traditional MCP implementations require static configuration and restarting the assistant when adding new tools. MCP-Codex enables dynamic discovery and execution, reducing operational complexity.
@twolven
基本信息
A MCP server for calling MCP tools remotely without requiring installation.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-codex": {
"command": "python",
"args": [
"codex-serv.py"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
MCP-Codex is a service for orchestrating Model Context Protocol (MCP) servers, allowing AI assistants to dynamically discover and utilize tools without requiring restarts. It features “The Librarian,” an intelligent agent that understands user needs and connects them with the right tools. This project is discontinued and shared as an educational resource.
Install by cloning the repository, running pip install -r requirements.txt, creating config, data, logs, tools directories, and configuring the .env file. Start the Codex Service with python codex-serv.py and the MCP Client with python mcp-codex.py. For Claude Desktop, use mcp install mcp-codex.py. The client registers two tools: search (find tools for a task) and call_tool (execute discovered tools).
Traditional MCP implementations require static configuration and restarting the assistant when adding new tools. MCP-Codex enables dynamic discovery and execution, reducing operational complexity.
The project consists of the Codex Service (core execution), the Librarian Service (planned AI-powered tool recommendation), and the MCP Client (interface for AI assistants).
The AI assistant uses the `search` tool to find relevant capabilities via natural language, then executes the discovered tool through the `call_tool` tool, which the Codex Service handles.
No. This project was discontinued after Cloudflare released a similar service. The code is shared as an educational resource and example of MCP orchestration architecture.
Python 3.10+ and required packages from `requirements.txt`. The tool registry is configured in `config/codex.yaml` and the environment via a `.env` file.
Turn any JavaScript module into a sandboxed MCP (Model Context Protocol) server with automatic reflection and type inference.
This is a very basic implementation of an Mcp-Reasoning-Server for Cursor AI .
A Node.js–based Model Context Protocol server that spins up disposable Docker containers to execute arbitrary JavaScript.
An MCP server that allows you to run TypeScript, JavaScript, and Python code in a sandbox on your local machine using the Deno® sandbox. This server provides a controlled environment for executing code with explicit permission controls.
This project demonstrates a decoupled real-time agent architecture that connects LangGraph agents to remote tools served by custom MCP (Modular Command Protocol) servers. The architecture enables a flexible and scalable multi-agent system where each tool can be hosted independent
评论