AI Search & Agents
Catering to the AI Overlords: What we know about GEO MCP optimization
Visibility is no longer only about ranking. As AI systems summarize sources, choose tools, and act through agents, websites and interfaces need to become understandable to both humans and machines.
This talk is for SEO, web, product, and engineering teams that want to understand how visibility changes through AI Overviews, generative search, chatbots, and AI agents. It is not about a new ranking trick, but about how content, websites, and tools become understandable to humans and machines. The session explains why classic SEO still matters, why GEO and LLMO shift attention toward citability, entities, and sources, and why MCP tool descriptions become a new interface layer for agents. Participants learn how structured content, clear information architecture, llms.txt, APIs, tool names, parameters, limitations, and examples fit together. The topic works as a conference talk, team impulse, deep dive, or workshop.
When AI systems choose sources and tools
- AI Overviews and chatbots answer complex questions directly, so the website click becomes less automatic.
- GEO, LLMO, AEO, llms.txt, and MCP are often mixed together, even though they solve different problems.
- Many websites, APIs, and MCP servers are technically reachable, but not semantically clear enough for models.
Audience
Who this is for
- SEO, content, web, and product teams
- Developer-tool, API, platform, and engineering teams
- Agencies, DevRel, technical writers, and website owners
- Organizations that want to understand AI search, GEO, and AI agents without buzzword fog
FAQ
What is the difference between SEO, GEO, and LLMO?
SEO optimizes visibility in search systems, while GEO and LLMO focus more on how content is found, understood, cited, and summarized in generative answers. The labels overlap, but the structural work matters more than the acronym.
How do you optimize content for AI Overviews?
Useful work includes clear information architecture, precise answers, explicit entities, credible sources, structured data, good internal links, and content with real subject-matter substance.
Does every website need an llms.txt?
Not necessarily. llms.txt can be an interesting orientation layer for LLMs, but it does not replace clean site structure, understandable content, or technical accessibility.
What does MCP have to do with SEO or GEO?
MCP concerns how agents use tools and data sources. Once AI systems do more than read content and start choosing tools, names, descriptions, parameters, and constraints become part of the visibility and interface question.
What makes a good MCP tool description?
A good tool description states purpose, suitable use cases, parameters, limits, exclusions, side effects, and examples. It helps models choose the right tool and avoid misunderstood calls.