AI Infrastructure

MCP Engine Optimization (MEO)

MCP Engine Optimization (MEO) is the discipline of designing an MCP server so that AI agents choose it over alternatives when answering a query.

MEO is to agents what SEO was to search engines. When an AI agent needs a fact, it picks a tool. That pick is a ranking decision. MEO is the engineering and positioning work that shapes which tool the agent reaches for, how the agent phrases the call, and how the returned result influences the rest of the loop.

The inputs to MEO include tool naming, description quality, schema clarity, cost of invocation, latency, citation density, and the structure of returned payloads. A well-optimized MCP describes exactly what it knows, returns exactly what the agent can act on, and costs less to use than the alternatives.

MEO matters because agents now mediate a growing share of decisions. If your company answers buyer questions through a competitor's MCP, you have lost distribution in a channel you did not know existed. MEO is the discipline that claims that distribution back.

The Amdahl view

MEO was coined at Amdahl during a 2026 internal conversation. The underlying bet: the next generation of agents will make thousands of tool-call decisions per query, and the winners will be MCPs deliberately designed to be picked. Amdahl's customer intelligence layer is built to win this for GTM queries. If a sales or marketing agent needs to know anything about a customer, we want to be the tool it reaches for first.

See customer intelligence running on your own customer conversations.