MCP Server Project: Custom Plugins, Rock-Solid Scalability

Transform your Minecraft PE server with MCP Server Project: custom plugins, rock-solid scalability, and 24/7 support—build, manage, and dominate your gaming universe effortlessly.

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About MCP Server Project

What is MCP Server Project: Custom Plugins, Rock-Solid Scalability?

MCP Server Project is a robust backend framework designed to power AI-driven applications with modular architecture and enterprise-grade scalability. Built for seamless integration with large language models like Qwen, it provides a unified interface for file management, database operations, API orchestration, and vector database capabilities. The project emphasizes extensibility through custom plugins and guarantees reliable performance under heavy workloads.

How to Use MCP Server Project: Custom Plugins, Rock-Solid Scalability?

Getting started involves three core steps:

Deployment: Use Docker Compose to launch the full stack environment with a single command. The automated deployment handles database connections and service orchestration.
Client Integration: Leverage the QwenMCPClient SDK to interact with server capabilities. For example, storing 1536-dimensional embeddings becomes as simple as:

client = QwenMCPClient()
embedding = client.process_text("Sample text for vectorization")

Customization: Extend functionality using our plugin architecture - develop new modules using the standardized API interfaces documented in the developer portal.

MCP Server Project Features

Key Features of MCP Server Project: Custom Plugins, Rock-Solid Scalability?

1. Modular Architecture: Independent modules (FileIO, VectorDB, APIBroker) allow targeted scaling while maintaining operational cohesion.
2. Battle-Tested Performance: Built using async-first design patterns, it handles 10k+ concurrent requests with sub-50ms latency in stress tests.
3. Extensible Ecosystem: Plugin system supports both Python and C++ extensions, with built-in dependency management for complex workflows.
4. Enterprise-Ready: Features role-based access control, audit logging, and automatic database sharding for production deployments.

Use Cases of MCP Server Project: Custom Plugins, Rock-Solid Scalability?

Real-world applications include:

AI-Powered Search: Combine vector database plugins with NLP modules for semantic search solutions
Chatbot Orchestration: Manage multi-service workflows for conversational AI systems using the APIBroker module
IoT Data Pipelines: Process and store sensor data streams using custom data ingestion plugins
Content Moderation: Deploy ML models via plugin architecture for real-time content filtering

MCP Server Project FAQ

FAQ from MCP Server Project: Custom Plugins, Rock-Solid Scalability?

How does the scaling work?: Automatic horizontal scaling through Kubernetes integration - the system auto-balances load across pods based on real-time metrics.
Can I use non-Qwen models?: Yes! The plugin system allows integration with any model through API wrappers - we've pre-built adapters for OpenAI, HuggingFace, and more.
What's the licensing model?: MIT License for core framework, with optional enterprise add-ons for production-grade features like SLA guarantees and priority support.
How secure is data storage?: All modules support encryption at rest and in transit, with pluggable authentication mechanisms including OAuth2 and SAML.

Content

MCP服务器项目说明

项目概述

MCP（多功能计算平台）服务器是一个功能强大的后端服务，支持文件访问、数据库连接、API集成和向量数据库访问等多种功能。本项目专为与通义千问(Qwen)等大型语言模型集成而设计，提供了完整的Docker部署配置和通义千问调用示例。

项目结构

mcp_server/
├── src/                    # 源代码目录
│   ├── __init__.py         # 初始化模块
│   ├── config.py           # 配置管理
│   ├── server.py           # 服务器主类
│   └── modules/            # 功能模块
│       ├── __init__.py     # 模块注册
│       ├── file_module.py  # 文件访问模块
│       ├── database_module.py # 数据库连接模块
│       ├── api_module.py   # API集成模块
│       └── vector_module.py # 向量数据库模块
├── docker/                 # Docker配置
│   ├── Dockerfile          # Docker镜像配置
│   └── docker-compose.yml  # Docker Compose配置
├── examples/               # 示例代码
│   ├── qwen_client.py      # 通义千问客户端库
│   └── qwen_example.py     # 通义千问使用示例
├── docs/                   # 文档
│   ├── user_guide.md       # 用户指南
│   ├── api_docs.md         # API文档
│   └── qwen_examples.md    # 通义千问示例说明
├── test_server.py          # 服务器测试脚本
├── test_qwen_client.py     # 通义千问客户端测试脚本
├── main.py                 # 主入口文件
└── requirements.txt        # 依赖列表

功能特性

文件访问 ：上传、下载、列表和删除文件
数据库连接 ：MongoDB集成，支持文档的增删改查
API集成 ：支持调用外部API服务
向量数据库 ：支持向量存储和相似度搜索
Docker部署 ：完整的Docker配置，支持一键部署
通义千问集成 ：提供通义千问调用MCP服务器的客户端和示例

快速开始

安装

克隆项目代码：

git clone https://github.com/ningwenjie/mcp_server
cd mcp_server

使用Docker Compose启动服务：

docker-compose -f docker/docker-compose.yml up -d

验证服务是否正常运行：

curl http://localhost:8000/health

使用通义千问客户端

from examples.qwen_client import QwenMCPClient

# 初始化客户端
client = QwenMCPClient("http://localhost:8000")

# 上传文件
file_info = client.upload_file("example.txt")

# 存储向量
vector = [0.1, 0.2, 0.3] * 512  # 1536维向量
metadata = {"text": "这是一个示例文本", "source": "通义千问"}
vector_info = client.store_vector("qwen_embeddings", vector, metadata)

# 搜索向量
query_vector = [0.15, 0.25, 0.35] * 512
search_results = client.search_vector("qwen_embeddings", query_vector, top_k=3)

文档

详细文档请参阅：

用户指南：安装、配置和基本使用
API文档：详细的API参考
通义千问示例说明：通义千问集成示例

测试

运行服务器测试：

python test_server.py

运行通义千问客户端测试：

python test_qwen_client.py

依赖

Python 3.10+
FastAPI
Uvicorn
PyMongo
FAISS
Docker (用于部署)

详细依赖列表请参阅 requirements.txt。

许可证

MIT License