Figma MCP vs. Paper MCP: What Evals Actually Tell You
Jessica Wang compares Figma and Paper MCP workflows to show how explicit tasks, representative datasets and scoring criteria turn AI-tool selection into a repeatable evaluation.
Jessica Wang
Developer Advocate · Braintrust
16Sep
7:00 PM UTC · 60 min
What you’ll explore
1
Follow an evaluation
Compare two MCP tools through a defined task and observed results.
2
Choose evidence over impressions
Use structured comparisons to examine quality and consistency.
3
Design useful criteria
Define inputs, scoring and interpretation for AI-tool decisions.
About this session
Jessica Wang compares Figma and Paper MCP workflows to show how explicit tasks, representative datasets and scoring criteria turn AI-tool selection into a repeatable evaluation.
Guest speaker
Jessica Wang
Developer Advocate · Braintrust
Jessica develops education and tooling around AI evaluations. Her earlier work includes software engineering at Microsoft and DoorDash.
The Gen Academy
Hosted by Aishwarya Srinivasan and Arvind Narayanamurthy.
Aishwarya Srinivasan
AI Advisor / AI Educator
Aishwarya Srinivasan is the Co-Founder of The Gen Academy and a Developer Relations Lead at Nebius. Her experience spans Fireworks AI, Microsoft for Startups, Google Cloud and IBM, where she worked with developers and businesses on practical AI and machine-learning solutions. She holds a postgraduate degree in Data Science from Columbia University and teaches AI engineering, applied machine learning, generative AI and LLMOps.
Founder @ The Gen Academy | AI Solutions Architect | Ex - Adobe, Microsoft, IBM
Arvind brings a cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe, where he led data science teams and developed production-grade machine-learning solutions. He has built and deployed enterprise AI systems and focuses on translating AI concepts into practical, production-ready solutions. He holds a Master's from Carnegie Mellon University.
