What dependencies are required?
Node.js, `@modelcontextprotocol/sdk`, `express`, and `dotenv`. API keys for DeepSeek and a weather service are also needed.
@Hiohk
基本信息
一个基于MCP协议开发的LLM(大模型)服务端项目
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。
A scalable service framework based on the Model Context Protocol (MCP), providing standardized tool calling interfaces and AI integration. It includes a base service architecture, a weather service implementation example, and an intelligent dialogue processing pipeline.
Install dependencies (npm install @modelcontextprotocol/sdk express dotenv), configure environment variables (API keys for DeepSeek and a weather service), and start the main service with npm run start:prod. Custom MCP services can be created using the provided service template and integrated into the client.
Node.js, `@modelcontextprotocol/sdk`, `express`, and `dotenv`. API keys for DeepSeek and a weather service are also needed.
Follow the provided template: create a JavaScript file using `Server` from the MCP SDK, implement `ListToolsRequestSchema` and `CallToolRequestSchema` handlers, then integrate it into the client with `StdioClientTransport`.
Stdio transport is used for communication between the MCP Client and MCP Servers.
The default maximum context length is 5 dialogue turns.
The framework includes a request retry mechanism with default intervals of 500 ms, 1000 ms, and 1500 ms (3 attempts). Error codes follow the standard MCP error schema.

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