MCP Server

@eooo-io

代码仓库

基本信息

分类

云与基础设施

传输方式

stdio

作者

eooo-io

关于 MCP Server

A Dockerized MCP server setup to deploy onto remote development servers

#mcp #docker #model-context-protocol

MCP Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

MCP Server 工具

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。

概览

What is Model Context Protocol Server?

A containerized Model Context Protocol (MCP) server that enables communication between LLMs and tools through a standardized protocol. It provides both REST API and WebSocket interfaces for tool execution and context management, targeting developers building LLM-powered applications.

How to use Model Context Protocol Server?

Deploy the server using Docker Compose: clone the repository, create tools, context, and data directories, configure a .env file, then run docker-compose up --build. Tools are placed as Python scripts in the tools directory, and the server exposes REST endpoints on port 3000 and a WebSocket at ws://localhost:3000 for real‑time communication.

Key features of Model Context Protocol Server

Dual runtime environment (Node.js + Python)
WebSocket support for real‑time updates
REST API endpoints for tool management and execution
Context persistence and management via JSON files
Tool execution isolation inside the container
Easy deployment with Docker

Use cases of Model Context Protocol Server

Allow LLMs to invoke Python‑based tools through a standardized API
Maintain conversation or session context across multiple interactions
Enable real‑time tool execution and monitoring via WebSocket
Provide a consistent, containerized interface for integrating tools with any MCP‑compatible LLM

常见问题

What are the prerequisites to run Model Context Protocol Server?

You need Docker and Docker Compose installed on your system.

How do I add a new tool to the server?

Place a Python script in the `tools` directory. The script should accept JSON parameters via `sys.argv[1]` and return JSON output printed to stdout.

How is context managed in Model Context Protocol Server?

Context files are stored as JSON in the `context` directory, each with a unique ID. They can be retrieved via the `GET /api/context/:id` REST endpoint or through WebSocket messages.

How do I stop the server?

Run `docker-compose down` in the project directory.

What security measures are implemented?

Tool execution is isolated within the container, input validation is performed on all API endpoints, and context access can be restricted based on implementation needs.