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
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | ||
| question | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
askWeatherWith only one tool, there is no possibility of ambiguity or overlap between tools. The agent cannot misselect among multiple options since only 'askWeather' exists.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions. The name 'askWeather' stands alone without any inconsistencies.
The server name 'Weather & HR Management MCP Server' suggests a broad scope covering both weather and HR domains, but only one tool is provided. This is a significant mismatch, as a single tool cannot adequately cover such diverse functionalities, making it too few for the apparent scope.
The server implies coverage of weather and HR management, but only a weather-related tool is present, with no HR tools at all. This represents a severe gap, as the tool surface is incomplete for the stated purpose, likely causing agent failures in HR-related tasks.
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