You worked hard on SEO and landed on Google's first page. But in 2026, what happens when customers stop searching themselves and just tell an AI agent, "Find me the best email tool"? The agent doesn't use Google — it picks from the list of tools it has access to. YC calls this the "Agent Economy," and here's what SaaS builders need to do to get ready.
What Is It?
Up until now, SaaS discovery was built for humans. SEO, copywriting, word-of-mouth, ads — all of it was designed to catch a human eye. But as AI agents become autonomous economic actors, that's changing.
Here's how an agent actually picks a tool:
- Scans the list of available tools (MCP servers, API catalogs, etc.)
- Reads each tool's description, schema, and examples
- Picks the tool that best fits the current task
- Runs it → evaluates the result → switches if needed
Here's the thing — agents don't see your UI, they see your description and schema. No matter how beautiful your landing page is, if the tool description is vague, the agent will pick something else.
What Changes?
Yoko Li at a16z put it well in her MCP deep dive — "the competitive edge for developer-focused companies is shifting from 'the best API design' to 'the best collection of tools for agents.'"
This isn't just a tech trend — it's a shift in the SaaS business model itself:
| Dimension | Before (Human-focused) | Agent Era |
|---|---|---|
| Discovery | Google SEO, ads, word-of-mouth | MCP registries, tool descriptions, llms.txt |
| Selection criteria | UI/UX, brand, pricing | Description clarity, schema accuracy, response speed |
| Integration | Set up manually by humans | Auto-connected by agents (MCP) |
| Pricing model | Seat-based / subscription | API call-based, agents compare cost-efficiency |
| Role of docs | Reference material for humans | Execution instructions for agents |
Vercel is already moving on this. They've built an AEO (AI Engine Optimization) tracking system to measure how often coding agents discover and recommend Vercel. Early data shows that roughly 20% of coding agents perform web searches and recommend tools based on those results.
The New Stack compares MCP adoption to a "gold rush for agent SEO." Just like companies scrambled to get on Google in the early 2000s, the race to be discoverable by agents has officially started.
Getting Started
- Optimize Your Tool Descriptions
The first thing an agent reads when evaluating a tool is the description. Instead of "Email sending tool," write something like: "Composes marketing emails using HTML templates and sends them to recipient lists at a scheduled time. Supports open-rate tracking and A/B testing." Make it specific enough that an agent can judge when and why to use it. - Design Agent-Friendly APIs
APIs and tools aren't 1:1. A high-level action like draft_email_and_send() beats a bare send_email() because agents want to accomplish things in as few calls as possible. Keep your JSON schemas tight too — include required fields, optional fields, and examples. - Create an llms.txt File
Think of llms.txt as the AI equivalent of robots.txt. Drop it in your site root and LLMs and agents can understand your service far more accurately. Write up a service description, key features, and an API endpoint summary — in a format machines can easily parse. - Ship an MCP Server
MCP (Model Context Protocol) is the standard protocol for AI models to call external tools. Build an MCP server for your service and it becomes instantly usable from clients like Cursor and Claude Desktop. List it on MCP marketplaces like Smithery or mcpt to boost your discoverability. - Start Tracking AEO
Track how often agents discover and use your tool. You can build a full system like Vercel did, or start simple — filter your API logs for requests where the User-Agent is an agent. You can even A/B test which descriptions drive more selection.
The Core Principle of "Agent SEO"
A key insight from the HN discussion: different models can react differently to the same tool description. Claude, GPT, and Gemini each evaluate tools by their own criteria. Rather than trying to optimize for every model separately, write descriptions that are clear and specific about core functionality — that works across the board.
MCP Is Still Early
Key features like authentication, permissions, and multi-tenancy aren't standardized yet. A security review is non-negotiable before you ship an MCP server to production. Also, most MCP servers are local-first right now, so remote deployment needs extra infrastructure planning.
