MCP Servers Collection
@melkorCBA
关于 MCP Servers Collection
Here lies all the custom MCP Servers I have created
基本信息
配置
工具
未检测到工具
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 MCP Servers Collection?
A repository of custom MCP (Model Context Protocol) servers that extend AI capabilities with specialized services. The collection currently includes the meal-planer server, which integrates with the Nutritionix API to provide nutritional information and meal planning features.
How to use MCP Servers Collection?
Clone the repository, navigate to the desired MCP server directory, follow the setup instructions in that server’s README, then connect your AI assistant to the running server. Most servers require Node.js 18+ and pnpm, and some need API keys configured via environment variables.
Key features of MCP Servers Collection
- Contains custom MCP servers for specialized services
- Example server: meal-planer with Nutritionix API support
- Servers enable real‑time data access and actions
- Minimal setup: Node.js 18+, pnpm, optional API keys
- Each server has its own README and configuration files
Use cases of MCP Servers Collection
- Generate meal plans with specific calorie targets
- Calculate macro nutrition breakdowns (Carbs, Protein, Fat)
- Fetch nutritional information for food items
- Structure daily meals with percentage‑based calorie distribution
FAQ from MCP Servers Collection
What servers are included in the collection?
The repository currently contains the meal-planer server. More custom MCP servers may be added over time.
What are the runtime requirements for these servers?
Most servers require Node.js 18+, the pnpm package manager, and any necessary API keys (if the server connects to an external service).
How do I configure API keys for a server?
Check the server’s directory for a .env.example file; it lists the environment variables you need to set for API access.
How do I run one of these MCP servers?
Clone the repository, go to the server’s directory, follow the steps in its README, then start the server and connect your AI assistant to it.
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