MCP Dev Summit North America

The next wave of MCP servers face the same challenge: discoverability. If an LLM doesn't know when to invoke your tool, it doesn't exist. This will compound - ChatGPT has ~80 apps today, but as more LLMs launch app stores and MCP adoption accelerates, there will be millions.

Drawing from research optimizing ChatGPT apps since launch, this talk reveals metadata patterns that determine whether AI systems surface your tools - principles that apply directly to MCP server development.

What I'll cover:
- Two-channel discovery: How static search (registries i.e. ChatGPT's App Store) and dynamic invocation (LLM tool selection) require different optimization strategies.

- How LLMs read metadata: How tool names, descriptions, and parameters shape invocation decisions.

- The golden prompt set: Testing methodology using direct, indirect, and negative prompts to measure recall and precision.

- What converts: Fixed templates, workflow handoffs, context preservation vs anti-patterns that fail.

- Measuring success: Citation tracking, false positive monitoring, and iteration loops that compound discoverability gains.