CbetaMCP

🧠 MCP 聚合工具服务 / MCP Aggregated Tool Service

License Python FastAPI Dockerized

一个基于 FastAPI + fastapi_mcp 实现的多工具统一接入平台，支持模块化、自动注册与异步扩展。适用于将多个 AI 工具或微服务聚合为一个统一接口服务，支持标准化输入输出格式，便于前端集成或 LLM 系统调用。

A modular, extensible and FastAPI-based MCP (Multi-Component Platform) tool aggregation service. Easily connect and expose independent tools through standardized APIs. Perfect for frontend integration or large language model (LLM) orchestration.

🌟 功能特点 / Features

✅ 支持多工具自动注册（基于目录扫描）
✅ 所有接口统一 POST 方式 + BaseModel 校验
✅ 支持异步 httpx 接口调用
✅ 标准化 JSON 响应格式（success/error）
✅ Docker 一键部署支持
✅ 配套开发说明文档，便于扩展工具模块

📁 项目结构 / Project Structure

.
├── main.py                    # FastAPI 主程序，含 MCP 注册逻辑
├── tools/                     # 工具目录，每个文件一个功能
├── Dockerfile                 # 构建镜像用
├── docker-compose.yml         # 一键部署支持
├── mcp_tool_开发说明.md       # 开发者使用规范文档（中文）
└── README.md

🚀 快速开始 / Quick Start

🧰 依赖要求 / Requirements

Python 3.8+
pip
Docker / Docker Compose（可选）

📦 本地运行 / Local Dev

# 安装依赖
pip install -r requirements.txt

# 启动服务
python main.py

默认服务地址：http://localhost:8000/mcp

🐳 使用 Docker 部署 / Docker Deployment

# 构建 & 运行
docker-compose up --build -d

# 访问 MCP 工具服务
http://localhost:8000/mcp

🧱 工具模块开发规范 / Tool Module Guidelines

每个工具应放置于 tools/ 目录下（可多层嵌套），并包含：

使用 pydantic.BaseModel 定义参数；
使用 @__mcp_server__.tool() 注册工具函数；
返回 success_response() 或 error_response()；
（可选）异步调用外部接口 + 缓存结果。

示例参考：

from pydantic import BaseModel
from main import __mcp_server__, success_response

class MyParams(BaseModel):
    name: str

@__mcp_server__.tool()
async def hello_tool(params: MyParams):
    return success_response({"message": f"Hello {params.name}!"})

🔗 接口说明 / API Usage

所有工具接口统一通过 /mcp 路径访问，自动根据模块注册。

请求方式：POST
请求格式：application/json
响应格式：

{
  "status": "success",
  "result": {
    ...
  }
}

📚 文档参考 / Docs

📘 开发说明文档 (中文)
示例模块路径：tools/xxx/tool_name.py

📄 License

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A modular, extensible FastAPI-based platform that aggregates multiple AI tools and microservices into a unified interface with standardized I/O formats, perfect for frontend integration or LLM system orchestration.

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