Aarti Jha
Aarti Jha is a Principal Data Scientist at Red Hat, where she develops AI-driven solutions to streamline internal processes and reduce operational costs. She brings over 7 years of experience in building and deploying data science and machine learning solutions across industry domains. She is an active public speaker and frequently presents at developer and data-science conferences, focusing on practical approaches to applied AI and LLMs.
Red Hat
Principal Data Scientist
Session
Workshop Outline:
Phase 1: The Foundation
The "Why" & The Architecture: Brief overview of why standardized engineering (via FastAPI) is the secret sauce for moving AI from "cool demo" to "production tool."
Environment Check: Rapid-fire validation of Git, Python, VSCode, and Postman environments.
The Blueprint: Introduction to the template-mcp-server repository and its core components.
Phase 2: From Zero to Local
Cloning & Configuration: Initializing your local environment and understanding the configuration files.
The "Hello World" Run: Getting the base template running on your machine.
Walkthrough: Navigating the repository structure—where the logic lives and how the server communicates.
Phase 3: Building Your Custom Tool
The Code-Along: Step-by-step implementation of a custom "tool" within the MCP framework.
Logic Injection: Defining the tool's purpose, input parameters, and execution logic.
Live Testing: Running the server locally and verifying that the tool is discoverable and functional.
Phase 4: Integration & Orchestration
Connecting to the Agent: Integrating your local MCP server with an agent (like Cursor) or a remote orchestrator.
The Real-World Test: Watching the agent invoke your custom tool to solve a live task.
Containerisation: A quick look at the "deployment-ready" state—wrapping your server for enterprise scale.
Outcomes:
Development: Move from a blank slate to a FastAPI-based MCP server.
Extensibility: Learn exactly how to add new tools to existing templates.
Integration: Connect your server to real-world agents like Cursor.
Scalability: Leave with a containerised solution ready for production deployment.
Note: This is a high-speed lab. We prioritize doing over discussing. Ensure your Python/Git/VSCode/Postman is warmed up and ready to pull images!