The Fifth Elephant 2025 Annual Conference (18 & 19 July)
Less hype. More engineering.
Jul 2025
14 Mon
15 Tue
16 Wed
17 Thu
18 Fri
19 Sat 09:00 AM – 05:30 PM IST
20 Sun
shubhankar khare
@shubhankar_khare Co-Host
Submitted May 23, 2025
Large Language Models aren’t just for chat anymore, they can now take real action. With the rise of the Model-Context Protocol (MCP), it’s now possible to expose your backend services as tools that AI assistants like Claude can directly invoke. MCP bridges the gap between natural language and your actual systems.
HubSpot’s MCP enables users to manage CRM contacts and deals directly through AI conversations, while Atlassian’s MCP lets customers view assigned tasks, create issues, and update project status right from their LLM interface. Now, imagine giving your users the same power to interact with your applications.
In this session, we’ll explore how to bring this capability to your own systems. You’ll learn how to build a lightweight MCP server in Python that wraps your existing REST APIs, turning them into tools that AI assistants can use. By the end, you’ll be equipped to empower your users to interact with your systems through natural language
Chaitanya Anand is a Software Development Engineer at Atlan, an AI-native data workspace focused on data cataloging, discovery, quality, governance, and lineage. Atlan is rethinking how data teams operate by embedding AI directly into their workflows. At the intersection of LLM integration, developer tooling, and business system orchestration, Chaitanya works on real-world implementations of early-stage protocols like MCP.
A seasoned hackathon veteran with 8 wins under his belt, Chaitanya has also mentored and judged numerous hackathons, bringing a builder’s perspective to rapid prototyping and innovative solutions.
He’s hands-on with building, testing, and breaking MCP servers - often prototyping new ones to boost his own productivity and push what’s possible in AI-assisted engineering.
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Shubhankar is a Senior Software Engineer at Atlan, where he serves as a core contributor to AI initiatives across the platform. With deep expertise in the data industry, he is currently tinkering with RAGs, AI agents, and MCP protocol to enhance data workflows, user experiences and developer experiences.
As co-instructor alongside Chaitanya, Shubhankar brings practical insights from building and deploying AI solutions at scale. His focus on bridging theoretical AI concepts with real-world applications helps participants understand how to effectively integrate these technologies into their own projects and workflows.
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