Built using Node.js to provide a modular MCP server architecture that connects multiple data sources including real-time weather data, HR job applications, interview scheduling, and database-driven recruitment data through REST APIs and Server-Sent Events.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Weather & HR Management MCP Server what's the weather in New York and show today's interview schedule"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
I built a custom MCP (Model Context Protocol) Server using Node.js that connects multiple real-world data sources and APIs into a single, unified, AI-accessible system. The goal of this project was to provide structured and reliable context to AI assistants, enabling smarter automation and decision-making.
This MCP Server supports real-time weather data retrieval based on city names, delivering accurate temperature and weather conditions on demand. Alongside this, it integrates a database-driven HR module that manages job applications, tracks daily job-related activities, and retrieves up-to-date recruitment data.
The system also includes interview and schedule management, allowing recruiters and HR teams to access today’s interview schedules and job timelines from a centralized source. To ensure live and continuous updates, the platform uses Server-Sent Events (SSE) for real-time communication between services.
Designed with scalability in mind, the architecture follows a modular MCP server approach, where separate MCP services handle weather data, job applications, and scheduling independently. This makes it easy to extend the system with new services without impacting existing functionality.
Overall, this project demonstrates how MCP-based systems can power AI-ready platforms for recruitment, scheduling, and smart automation workflows by delivering clean, real-time, and well-structured contextual data. I built a custom MCP (Model Context Protocol) Server using Node.js that connects multiple real-world data sources and APIs into a single, unified, AI-accessible system. The goal of this project was to provide structured and reliable context to AI assistants, enabling smarter automation and decision-making. This MCP Server supports real-time weather data retrieval based on city names, delivering accurate temperature and weather conditions on demand. Alongside this, it integrates a database-driven HR module that manages job applications, tracks daily job-related activities, and retrieves up-to-date recruitment data. The system also includes interview and schedule management, allowing recruiters and HR teams to access today’s interview schedules and job timelines from a centralized source. To ensure live and continuous updates, the platform uses Server-Sent Events (SSE) for real-time communication between services. Designed with scalability in mind, the architecture follows a modular MCP server approach, where separate MCP services handle weather data, job applications, and scheduling independently. This makes it easy to extend the system with new services without impacting existing functionality. Overall, this project demonstrates how MCP-based systems can power AI-ready platforms for recruitment, scheduling, and smart automation workflows by delivering clean, real-time, and well-structured contextual data. Skills: Model Context Protocol (MCP) · Node.js · Server-Sent Events (SSE) · REST APIs · Database Design & Integration · AI Tooling & Context Engineering
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
16 AI-native tools with dual SSE + streamable-http transport. Free tier available.
Swiss weather data for AI assistants — forecasts, measurements, stations, pollen.
Connect AI assistants to Stellary projects, boards, documents, and governed agent workflows.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/yashdhokane/Custom-MCP-Server-for-Weather-Data-HR-Modules'
If you have feedback or need assistance with the MCP directory API, please join our Discord server