LLMs + GraphQL + MCP: A Blueprint for Scalable AI Tooling

September 10, 3:50 – 4:20 PM

IJzaal

AI / LLM

Session description

Plugging an LLM into GraphQL sounds simple—until it drowns in thousands of fields, types, and connections. Most models today can’t reason effectively over large APIs without brittle prompt hacks or hardcoded shortcuts. Model Context Protocol (MCP) is the cutting-edge solution for enabling seamless, dynamic interactions between LLMs and external tooling. It standardizes the way models interact with various tools, breaking down barriers between APIs and AI systems. In this talk, you’ll discover how to turn any GraphQL endpoint into an MCP-compatible server with minimal overhead. Reuse your existing GraphQL infrastructure to avoid reinventing authorization, schema management, and validation enabling scalable, robust LLM integrations. We’ll compare existing tools and automated schema discovery against hand-crafted mappers based on benchmarks of public GraphQL APIs. Join us to learn about our experiences and recommendations for your next GenAI project, powered by GraphQL.

Session speakers

Erik Wrede

Strawberry-GraphQL, Software Engineer

returning speaker

Erik is a Software Engineer and GraphQL enthusiast that enjoys building full-stack GraphQL solutions. As a member of the GraphQL-Python Maintainer Team and Core Dev at Strawberry-GraphQL, he’s passionate about improving the developer experience and creating exciting new GraphQL tooling. Erik is excited about building performant and scalable solutions and is always eager to chat about new features, developments and the latest advancements in tech.

Thore Koritzius

Independent, Machine Learning Software Engineer

Thore is an ML Engineer with a passion for building and deploying multimodal LLMs. He enjoys working on the full stack of AI systems—from training embedding models and optimizing RAG pipelines to deploying on-premises LLM infrastructure. Thore is also a GraphQL and Rust enthusiast who loves exploring modern developer tools and building high-performance systems. His journey into AI started with research on Physics-Informed Neural Networks during his Master’s thesis, and he's been excited about pushing the boundaries of applied machine learning ever since.

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